diff --git a/.circleci/config.yml b/.circleci/config.yml index 64e666e085..f866361569 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -144,7 +144,7 @@ jobs: python bin/generate.py --path . --output package -v # make a copy of use_case_lib in order to have ES work :-( cp package/default/use_case_library.conf package/default/analyticstories.conf - - run: + - run: name: copy lookups .csv files command: | cd security-content @@ -261,17 +261,19 @@ jobs: - run: *apt-install - aws-cli/setup: profile-name: default - - run: + - run: name: update community api sources command: | cd security-content - aws s3 cp stories s3://security-content-community/stories --recursive --exclude "*" --include "*.yml" - aws s3 cp baselines s3://security-content-community/baselines --recursive --exclude "*" --include "*.yml" - aws s3 cp detections s3://security-content-community/detections --recursive --exclude "*" --include "*.yml" - aws s3 cp investigations s3://security-content-community/investigations --recursive --exclude "*" --include "*.yml" - aws s3 cp responses s3://security-content-community/responses --recursive --exclude "*" --include "*.yml" - aws s3 cp lookups s3://security-content-community/lookups --recursive --exclude "*" --include "*.yml" - aws s3 cp macros s3://security-content-community/macros --recursive --exclude "*" --include "*.yml" + aws s3 cp stories s3://security-content/stories --recursive --exclude "*" --include "*.yml" + aws s3 cp baselines s3://security-content/baselines --recursive --exclude "*" --include "*.yml" + aws s3 cp detections s3://security-content/detections --recursive --exclude "*" --include "*.yml" + aws s3 cp response_tasks s3://security-content/response_tasks --recursive --exclude "*" --include "*.yml" + aws s3 cp responses s3://security-content/responses --recursive --exclude "*" --include "*.yml" + aws s3 cp lookups s3://security-content/lookups --recursive --exclude "*" --include "*.yml" + aws s3 cp lookups s3://security-content/lookups --recursive --exclude "*" --include "*.csv" + aws s3 cp macros s3://security-content/macros --recursive --exclude "*" --include "*.yml" + aws s3 cp deployments s3://security-content/deployments --recursive --exclude "*" --include "*.yml" - persist_to_workspace: root: ~/ paths: @@ -351,7 +353,7 @@ jobs: - run: name: sync latest ESCU to the Attack Range S3 bucket for apps command: | - aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ + aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ # make the file public since it is not by default aws s3api put-object-acl --bucket attack-range-appbinaries --key DA-ESS-ContentUpdate-latest.tar.gz --acl public-read master-api-update: @@ -368,18 +370,19 @@ jobs: - run: *apt-install - aws-cli/setup: profile-name: default - - run: + - run: name: update api sources command: | cd security-content aws s3 cp stories s3://security-content/stories --recursive --exclude "*" --include "*.yml" aws s3 cp baselines s3://security-content/baselines --recursive --exclude "*" --include "*.yml" aws s3 cp detections s3://security-content/detections --recursive --exclude "*" --include "*.yml" - aws s3 cp investigations s3://security-content/investigations --recursive --exclude "*" --include "*.yml" + aws s3 cp response_tasks s3://security-content/response_tasks --recursive --exclude "*" --include "*.yml" aws s3 cp responses s3://security-content/responses --recursive --exclude "*" --include "*.yml" aws s3 cp lookups s3://security-content/lookups --recursive --exclude "*" --include "*.yml" + aws s3 cp lookups s3://security-content/lookups --recursive --exclude "*" --include "*.csv" aws s3 cp macros s3://security-content/macros --recursive --exclude "*" --include "*.yml" - + aws s3 cp deployments s3://security-content/deployments --recursive --exclude "*" --include "*.yml" workflows: version: 2.1 validate-and-build: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4bb6720b88..b46d0df484 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,7 +9,4 @@ repos: - id: check-yaml - id: pretty-format-json args: [--autofix] - - id: flake8 - args: [--max-line-length=131] - exclude: 'package/bin/da_ess_contentupdate/|package/bin/splunklib/|venv/|package/bin/escu_contextualize.py|package/bin/escu_investigate.py|package/bin/runstory.py|package/bin/detect.py|package/bin/investigate.py' - id: requirements-txt-fixer diff --git a/README.md b/README.md index d813d6b133..64f9750747 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ - - - + + + # Splunk Security Content -![security-content](docs/static/logo.png) +![security-content](docs/static/logo.png) ===== | branch | build status | @@ -10,81 +10,60 @@ | develop| [![develop status](https://circleci.com/gh/splunk/security-content/tree/develop.svg?style=svg&circle-token=67ad1fa7779c57d7e5bcfc42bd617baf607ec269)](https://circleci.com/gh/splunk/security-content/tree/develop)| | master | [![master status](https://circleci.com/gh/splunk/security-content/tree/master.svg?style=svg&circle-token=67ad1fa7779c57d7e5bcfc42bd617baf607ec269)](https://circleci.com/gh/splunk/security-content/tree/master)| -Welcome to the Splunk Security Research Team's Security Content Exchange! - -This project gives you access to our repository of Analytic Stories--themed security guides that contain that provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all designed to work together to detect, investigate, and respond to threats. - -While this content is available via Splunk Enterprise Security and Enterprise Security Content Updates (https://splunkbase.splunk.com/app/3449/), we have now made it available as an open-source project (which you just found--hi!). The Security Research Content Exchange was designed to bring the community together to improve our collective defenses. By sharing research and analytics, we can help the entire industry craft more effective strategies. This project provides a mechanism to facilitate this exchange. +Welcome to the Splunk Security Content +This project gives you access to our repository of Analytic Stories that are security guides which provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all designed to work together to detect, investigate, and respond to threats. # Usage -The Splunk Security Content Exchange can be used via: +The Splunk Security Content can be used via: #### [Splunk App](https://github.com/splunk/security-content/releases) -Grab the latest release of DA-ESS-ContentUpdate and install it on a Splunk Enterprise server (search head). +Grab the latest release of DA-ESS-ContentUpdate and install it on a Splunk Enterprise instance. #### [API](https://docs.splunkresearch.com/?version=latest) ``` curl -s https://content.splunkresearch.com | jq { - "hello": "welcome to Splunks Research security content api", - "available_endpoints": [ - "/stories", - "/detections", - "/investigations", - "/baselines", - "/responses", - "/package" - ] + "hello": "welcome to Splunks Research security content api" } ``` -# How to Get Started -While you you can configure each detection individually, the real power lies in running the Analytic Stories end-to-end. Using the Analytic Story Execution (ASX) app (https://github.com/splunk/analytic_story_execution), you can funnel the results to investigations, reducing the need for manual intervention. When available, you can automatically trigger reponses, as well. The alerts you'll get include context (history, correlations, etc.), so they help you better understand their importance. The net effect is a more efficient workflow, as well as more comprehensive, effective defenses. - -Follow the instructions below to get started. +#### [GitHub Workflow](https://github.com/splunk/security-content/wiki/2.-Installation-and-Usage) +Create your customized version of Security Content by forking this project and following this guide. # What's in an Analytic Story? -[Analytic Stories](https://github.com/splunk/security-content/blob/develop/docs/stories_categories.md) and their corresponding searches are composed of **.yml** files (manifests) and associated .conf files. The stories reside in [/stories](https://github.com/splunk/security-content/tree/develop/stories) and the searches live in [/detections](https://github.com/splunk/security-content/tree/develop/detections). +[Analytic Stories](https://github.com/splunk/security-content/blob/develop/docs/stories_categories.md) and their corresponding searches are composed of **.yml** files (manifests) and associated .conf files. The stories reside in [/stories](https://github.com/splunk/security-content/tree/develop/stories) and the searches live in [/detections](https://github.com/splunk/security-content/tree/develop/detections). Manifests contain a number of mandatory and optional fields. You can see the full field list for each piece of content [here](https://github.com/splunk/security-content/tree/develop/docs#spec-documentation). # Customize to your Environment -After release [1.0.46](https://github.com/splunk/security-content/releases) we introduced a concept of input(pre-filter) and output(post-filter) macros for each of our detection search. The intention behind introducing these macros is primarily to help our users to update the macro definition “once” and those changes will be applicable across all detections that leverage that macro and local to your Splunk Environment. +After release [1.0.46](https://github.com/splunk/security-content/releases) we introduced a concept of **input(pre-filter)** and **output(post-filter)** macros for each of our detection search. The intention behind introducing these macros is primarily to help our users to update the macro definition “once” and those changes will be applicable across all detections that leverage that macro and local to your Splunk Environment. -**input(pre-filter):** This macro is to specify your environment-specific configurations (index, source, sourcetype, etc.) to get the specific data sources that you would like to bring in. Replace the macro definition with configurations for your Splunk environment. +**input(pre-filter):** This macro is to specify your environment-specific configurations (index, source, sourcetype, etc.) to get the specific data sources that you would like to bring in. Replace the macro definition with configurations for your Splunk environment. For example the [sysmon](macros/sysmon.yml) **input macro** can be modified to the local splunk deployments index or sourcetype. -**output(post-filter):** This macro is to specify your environment-specific values (eg: dest, user), to filter out known false positives.. Replace the macro definition with values that you’d like to exclude from detection results. Think of this as a whitelisting/blacklisting using macros. +**output(post-filter):** This macro is to specify your environment-specific values (eg: dest, user), to filter out known false positives.. Replace the macro definition with values that you’d like to exclude from detection results. Think of this as a whitelisting/blacklisting using macros. A good example -Note: we are currently working on coming up with a better naming convention and making this consistent across all our detections, investigations and baselines. Suggestions are welcomed :stuck_out_tongue: # Execute an Analytic Story -Download and install the latest version of [Splunk Analytic Story Execution] +Download and install the latest version of [Splunk Analytic Story Execution] (https://github.com/splunk/analytic_story_execution/releases). This Splunk application will help the user do the following: 1. Execute an analytic story in an adhoc mode and view the results. 2. Schedule all the detection searches in an analytic story. 3. Update security-content via an API - + # Writing Content -Before you begin, follow the steps to install **dependencies and pre-commit hooks** under ["Developing"](https://github.com/splunk/security-content#developing). +Before you begin, follow the steps to install **dependencies and pre-commit hooks** under ["Developing"](https://github.com/splunk/security-content#developing). -1. Select the content [piece](https://github.com/splunk/security-content#content-parts) you want to write. -2. Copy an example and edit it to suit your needs. At a minimum, you must write a [story](stories/), [a detection search](detections/), and an [investigative search](investigations/). -3. Make a pull request. The pull request will trigger CircleCI, a continuous-integration app thatintegrates with a VCS and automatically runs a series of steps every time that it detects a change to your repository. A CircleCI build consists of a series of steps, usually Dependencies, Testing, and Deployment. If your tests pass, you're good to go! If the CircleCI check fails, refer to [troubleshooting](https://github.com/splunk/security-content#troubleshooting). - -For a more detailed explanation on how to contribute to the project, please see ["Contributing"](#Contributing) - -# Security Content Layout -![](docs/static/structure.png) +# Security Content #### Content Parts -* [stories/](stories/): All Analytic Stories +* [stories/](stories/): All Analytic Stories * [detections/](detections/): Splunk Enterprise, Splunk UBA, and Splunk Phantom detections that power Analytic Stories -* [investigations/](investigations/): Splunk Enterprise and Splunk Phantom investigative searches and playbooks employed by Analytic Stories +* [response_tasks/](response_tasks/): Splunk Enterprise and Splunk Phantom investigative searches and playbooks employed by Analytic Stories * [responses/](responses/): Automated Splunk Enterprise and Splunk Phantom responses triggered by Analytic Stories * [baselines/](baselines/): Splunk Phantom and Splunk Enterprise baseline searches needed to support detection searches in Analytic Stories @@ -92,46 +71,8 @@ For a more detailed explanation on how to contribute to the project, please see * [package/](package/): Splunk content app-source files, including lookups, binaries, and default config files * [bin/](bin/): All binaries required to produce and test content -# Docs -* [docs/](docs/): Documentation for all spec files -* [spec/](spec/): All spec files that describe the security content - -# Developing -##### Dependecies and Pre-Commit Hooks -Install project dependecies and tests that run before content is committed: - -1. Create virtualenv and install requirements: `virtualenv venv && source venv/bin/activate && pip install -r requirements.txt`. -2. Install `pre-commit install`. - -##### CI Tools -Tools that help with testing CI jobs: - -1. Install CircleCI [CLI Tool](https://circleci.com/docs/2.0/local-cli/). -2. To test a local change to CircleCI or build, make sure you are running Docker, then enter -`circleci local execute -e GITHUB_TOKEN=$GITHUB_TOKEN --branch `. - -##### Generate Docs from Schema -To automatically generate docs from schema: - -1. Install https://github.com/adobe/jsonschema2md. -2. Enter `jsonschema2md -d spec/v2/detections.spec.json -o docs`. - -# Troubleshooting - -### Our Automated Tests -1. [CI](https://github.com/splunk/security-content/blob/44946063173f7bc9921f0da0aa62139c084d1c51/.circleci/config.yml#L27) validates that the content was written to spec using [`validate.py`](https://github.com/splunk/security-content/blob/runstory/bin/generate.py). To run validation manually, run: `python bin/validate.py --path . --verbose`. -2. [CI](https://github.com/splunk/security-content/blob/44946063173f7bc9921f0da0aa62139c084d1c51/.circleci/config.yml#L60) generates Splunk configuration files using [`generate.py`](https://github.com/splunk/security-content/blob/develop/bin/generate.py). If you want to export Splunk .conf files manually from the content, run `python bin/generate.py --path . --output package --verbose`. -3. [CI](https://github.com/splunk/security-content/blob/44946063173f7bc9921f0da0aa62139c084d1c51/.circleci/config.yml#L107) builds a DA-ESS-ContentUpdate Splunk package using the [Splunk Packaging Toolkit](http://dev.splunk.com/view/packaging-toolkit/SP-CAAAE9V). -4. [CI](https://github.com/splunk/security-content/blob/44946063173f7bc9921f0da0aa62139c084d1c51/.circleci/config.yml#L145) tests the newly produced package using [Splunk Appinspect](http://dev.splunk.com/view/appinspect/SP-CAAAE9U). - -* note that [requirements.txt](https://github.com/splunk/security-content/blob/develop/requirements.txt) hard codes the versions for packages we use [dependabot](https://dependabot.com/) to make sure we safely always upgrade to the latest versions. - -## Customize to Your Environment -Release 1.0.46 introduced input(pre-filter) and output(post-filter) macros for each of our detection searches. These macros let you update a macro definition once and then apply the new definition across all detections that leverage that macro. These changes will be local to your Splunk environment. - -input(pre-filter): This macro specifies your environment-specific configurations (index, source, sourcetype, etc.) to get the specific data sources that you require. Replace the macro definition with configurations for your Splunk environment. -output(post-filter): This macro specifies your environment-specific values (dest, user, etc,), to filter out known false positives. Replace the macro definition with values that you'd like to exclude from detection results. Think of this as whitelisting/blacklisting using macros. -Note: Coming soon is an improved naming convention that will be consistent across all of our detections, investigations, and baselines. +# Contribution +We welcome feedback and contributions from the community! Please see our [contribution guidelines](docs/CONTRIBUTING.md) for more information on how to get involved. ## Support Please use the [GitHub Issue Tracker](https://github.com/splunk/security-content/issues) to submit bugs or request features. @@ -141,9 +82,3 @@ If you have questions or need support, you can: * Post a question to [Splunk Answers](http://answers.splunk.com) * Join the [#security-research](https://splunk-usergroups.slack.com/messages/C1RH09ERM/) room in the [Splunk Slack channel](http://splunk-usergroups.slack.com) * If you are a Splunk Enterprise customer with a valid support entitlement contract and have a Splunk-related question, you can also open a support case on the https://www.splunk.com/ support portal - -## Contributing -We welcome feedback and contributions from the community! Please see our [contribution guidelines](docs/CONTRIBUTING.md) for more information on how to get involved. - -# To Dos -* Build CLI for interacting and developing diff --git a/baselines/add_prohibited_processes_to_enterprise_security.yml b/baselines/add_prohibited_processes_to_enterprise_security.yml new file mode 100644 index 0000000000..a76e2c7324 --- /dev/null +++ b/baselines/add_prohibited_processes_to_enterprise_security.yml @@ -0,0 +1,20 @@ +name: Add Prohibited Processes to Enterprise Security +id: 251930a5-1451-4428-bb13-eed5775be0ce +version: 1 +date: '2017-09-15' +description: This search takes the existing interesting process table from ES, filters + out any existing additions added by ESCU and then updates the table with processes + identified by ESCU that should be prohibited on your endpoints. +how_to_implement: This search should be run on each new install of ESCU. +author: David Dorsey, Splunk +search: '| inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup + append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull + value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup + interesting_processes_lookup | stats count' +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Monitor for Unauthorized Software + - SamSam Ransomware + detections: + - Prohibited Software On Endpoint diff --git a/baselines/add_prohibited_processes_to_es.yml b/baselines/add_prohibited_processes_to_es.yml deleted file mode 100644 index e240f49f8a..0000000000 --- a/baselines/add_prohibited_processes_to_es.yml +++ /dev/null @@ -1,43 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup - append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain - | fillnull value=false is_required is_secure | fillnull value=true is_prohibited - | outputlookup interesting_processes_lookup | stats count' -creation_date: '2017-06-27' -data_metadata: - data_models: [] - data_source: - - Splunk Enterprise - providing_technologies: - - Splunk Enterprise Security -description: This search takes the existing interesting process table from ES, filters - out any existing additions added by ESCU and then updates the table with processes - identified by ESCU that should be prohibited on your endpoints. -eli5: This search outputs the interesting processes lookup table and filters out all - processes in the table that haven't already been inserted by ESCU. It then appends - to those results all the processes currently identified by ESCU that should be prohibited. - Next, it fills in the required fields with processes identified by ESCU, and then - writes the results back to the interesting process lookup table. This is done so - any new processes identified that should be prohibited will be added to the lookup - table without creating any duplicate entries. -how_to_implement: This search should be run on each new install of ESCU. -id: 251930a5-1451-4428-bb13-eed5775be0ce -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-15' -name: Add Prohibited Processes to Enterprise Security -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/approved_aws_service_accounts.yml b/baselines/approved_aws_service_accounts.yml deleted file mode 100644 index 65f5338fe8..0000000000 --- a/baselines/approved_aws_service_accounts.yml +++ /dev/null @@ -1,45 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail errorCode=success | rename userName as identity - | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats - count by identity | table identity | outputlookup aws_service_accounts | stats - count -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for successful API activity in CloudTrail within the - last 30 days, filters out known users from the identity table, and outputs values - of users into `aws_service_accounts.csv` lookup file. -eli5: We first look for all successful CloudTrail API activity caused by types of - user accounts and then remove all the events caused by users in the Identity table. - This generates a list of accounts--typically service accounts--configured in your - AWS environment. We output this list of service accounts to `aws_service_accounts.csv`. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Please validate the service account entires in `aws_service_accounts.csv`, - which is a lookup file created as a result of running this support search. Please - remove the entries of service accounts that are not legitimate. -id: fc0edc95-ff2b-48b1-5f6f-63ga3789fd43 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-12-03' -name: Create a list of approved AWS service accounts -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/baselines/assets_sorted_by_category.yml b/baselines/assets_sorted_by_category.yml deleted file mode 100644 index 770e29c9a6..0000000000 --- a/baselines/assets_sorted_by_category.yml +++ /dev/null @@ -1,40 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| from datamodel Identity_Management.All_Assets | stats count values(nt_host) - by category | sort -count' -creation_date: '2017-06-11' -data_metadata: - data_models: - - Identity_Management - data_source: - - Splunk Enterprise Security - providing_technologies: - - Splunk Enterprise Security -description: This search shows you every asset category you have and the assets that - belong to those categories. -eli5: This search gives you the number and the names of the hosts of each host in - your environment by category. It will then sort them by the count. -how_to_implement: To successfully implement this search you must first leverage the - Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv - file which should then be mapped to the Identity_Management data model. The Identity_Management - data model will contain a list of known authorized company assets. Ensure that all - inventoried systems are constantly vetted and updated. -id: dcfd6b40-42f9-469d-a433-2e53f7489ff9 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-13' -name: Count of assets by category -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_api_calls_per_user_arn.yml b/baselines/baseline_api_calls_per_user_arn.yml deleted file mode 100644 index ab8128d0e7..0000000000 --- a/baselines/baseline_api_calls_per_user_arn.yml +++ /dev/null @@ -1,47 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn - | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) - as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, - stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, - avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats - count -creation_date: '2018-04-09' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search establishes, on a per-hour basis, the average and the standard - deviation of the number of API calls made by each user. Also recorded is the number - of data points for each user. This table is then outputted to a lookup file to allow - the detection search to operate quickly. -eli5: This search returns all log events that are API calls, pulls out the ARN that - initiated each call, and collects them in one-hour groupings. Next, it calculates - the number of API calls made per ARN per hour. For each ARN, it calculates the average - and standard deviation of this count on a per-hour basis. It also includes the - number of data points each ARN had. This table is then stored in a lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. -id: fc0edc96-ff2b-48b0-9f6f-63da3783fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-04-09' -name: Baseline of API Calls per User ARN -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_aws_regions.yml b/baselines/baseline_aws_regions.yml deleted file mode 100644 index cebfa4c5a9..0000000000 --- a/baselines/baseline_aws_regions.yml +++ /dev/null @@ -1,45 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest - latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv - | stats count -creation_date: '2018-01-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where an AWS instance is started - and creates a baseline of most recent time (latest) and the first time (earliest) - we've seen this region in our dataset grouped by the value awsRegion for the last - 30 days -eli5: In this support search, we create a table of the first time (earliest) and most - recent time (latest) that this region has been seen in our dataset, grouped by the - value `awsRegion`. We only look for those events where an instance has been started. - All of these entries will be added to the `previously_seen_aws_regions.csv` lookup - file, which will act like a baseline for detections. Please validate the entries - of region names in the lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. -id: fc0edc95-ff2b-48b0-9f6f-63da3789fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-01-08' -name: Previously Seen AWS Regions -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_blocked_vpc_traffic.yml b/baselines/baseline_blocked_vpc_traffic.yml deleted file mode 100644 index fa151bc2ce..0000000000 --- a/baselines/baseline_blocked_vpc_traffic.yml +++ /dev/null @@ -1,54 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 - OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND - dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | - stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) - as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) - as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections - by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, - stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections - | stats count -creation_date: '2018-04-26' -data_metadata: - data_source: - - AWS VPC Flow Logs - data_sourcetypes: - - aws:cloudwatchlogs:vpcflow - providing_technologies: - - AWS -description: This search establishes, on a per-hour basis, the average and the standard - deviation of the number of outbound connections blocked in your VPC flow logs by - each source IP address (IP address of your EC2 instances). Also recorded is the - number of data points for each source IP. This table outputs to a lookup file to - allow the detection search to operate quickly. -eli5: Use this search to create a baseline of blocked outbound network connections - by each source IP in your AWS environment. This search returns all log events that - correspond to a blocked outbound network connection, extracts the source IP from - where the outbound connection was initiated, and collects the events in one-hour - groupings. Next, it calculates the number of outbound connections blocked per hour. - For each source IP, it calculates the average and standard deviation of this count - on a per-hour basis. It also includes the number of data points each source IP - had. This table is then stored in a lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow - logs.`. -id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-07' -name: Baseline of blocked outbound traffic from AWS -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_cloud_compute_regions.yml b/baselines/baseline_cloud_compute_regions.yml deleted file mode 100644 index d3a6f872fa..0000000000 --- a/baselines/baseline_cloud_compute_regions.yml +++ /dev/null @@ -1,50 +0,0 @@ -baseline: - splunk: - lookups: - - previously_seen_cloud_regions - macros: - - previously_seen_cloud_regions_input_filter - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from - datamodel=Cloud_Infrastructure.Compute where Compute.action=start `previously_seen_cloud_regions_input_filter` - by Compute.region | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_regions - | stats count' -creation_date: '2019-10-02' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search looks for cloud compute events where a compute instance is - started and creates a baseline of most recent time, `lastTime` and the first time - `firstTime` we've seen this region in our dataset grouped by the region for the - last 30 days -eli5: In this support search, we create a table of the first time `firstTime` and - most recent time `lastTime` that this region has been seen in our dataset, grouped - by the region. We only look for those events where an instance has been started. - All of these entries will be added to the `previously_seen_cloud_regions` lookup - file, which will act like a baseline for detections. -how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs - and have the Security Research cloud data model installed. -id: b5e232db-dec6-4db8-aaa1-dd5474521e40 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-10-02' -name: Previously Seen Cloud Regions -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_network_acl_modifications.yml b/baselines/baseline_network_acl_modifications.yml deleted file mode 100644 index 9b97d69b7d..0000000000 --- a/baselines/baseline_network_acl_modifications.yml +++ /dev/null @@ -1,49 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `network_acl_events` | spath output=arn path=userIdentity.arn - | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) - as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, - stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, - avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats - count -creation_date: '2018-05-21' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search establishes, on a per-hour basis, the average and the standard - deviation of the number of API calls that were related to network ACLs made by each - user. Also recorded is the number of data points for each user. This table is then - outputted to a lookup file to allow the detection search to operate quickly. -eli5: Use this search to create a baseline for API calls related to network ACLs for - the users who initiated this activity. It returns all logged API calls for network - activity, pulls out the ARN that initiated each call, and collects the `eventNames` - in one-hour groupings. Next, it calculates the number of API calls made per ARN - per-hour. For each ARN, it calculates the average and standard deviation of this - count on a per-hour basis. It also includes the number of data points for each ARN. - This table is stored in a lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. -id: fc0edd96-ff2b-4810-9f1f-63da3783fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-21' -name: Baseline of Network ACL Activity by ARN -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_of_api_calls_per_user_arn.yml b/baselines/baseline_of_api_calls_per_user_arn.yml new file mode 100644 index 0000000000..70a7a6e837 --- /dev/null +++ b/baselines/baseline_of_api_calls_per_user_arn.yml @@ -0,0 +1,22 @@ +name: Baseline of API Calls per User ARN +id: fc0edc96-ff2b-48b0-9f6f-63da3783fd63 +version: 1 +date: '2018-04-09' +description: This search establishes, on a per-hour basis, the average and the standard + deviation of the number of API calls made by each user. Also recorded is the number + of data points for each user. This table is then outputted to a lookup file to allow + the detection search to operate quickly. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +search: '`cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn + | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) + as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, + stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, + avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count' +tags: + analytics_story: + - AWS User Monitoring + detections: + - Detect Spike in AWS API Activity diff --git a/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml new file mode 100644 index 0000000000..a4f764b1af --- /dev/null +++ b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml @@ -0,0 +1,28 @@ +name: Baseline of blocked outbound traffic from AWS +id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63 +version: 1 +date: '2018-05-07' +description: This search establishes, on a per-hour basis, the average and the standard + deviation of the number of outbound connections blocked in your VPC flow logs by + each source IP address (IP address of your EC2 instances). Also recorded is the + number of data points for each source IP. This table outputs to a lookup file to + allow the detection search to operate quickly. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow + logs.`. +author: Bhavin Patel, Splunk +search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 + OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) + | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip + | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) + as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) + as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, + avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections + | stats count' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - Suspicious AWS Traffic + detections: + - Detect Spike in blocked Outbound Traffic from your AWS diff --git a/baselines/train_unusually_long_commandlines.yml b/baselines/baseline_of_command_line_length___mltk.yml similarity index 51% rename from baselines/train_unusually_long_commandlines.yml rename to baselines/baseline_of_command_line_length___mltk.yml index f41188949c..28205e56d9 100644 --- a/baselines/train_unusually_long_commandlines.yml +++ b/baselines/baseline_of_command_line_length___mltk.yml @@ -1,34 +1,12 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -31d@d - latest_time: -1d@d - search: '| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as - end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest - Processes.process_name Processes.process | `drop_dm_object_name(Processes)` - | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) - | fit DensityFunction processlen by user into cmdline_pdfmodel' -creation_date: '2019-05-08' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Baseline of Command Line Length - MLTK +id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459 +version: 1 +date: '2019-05-08' description: This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. -eli5: Create a machine-learning (ML) model to characterize the length of the command - lines used in your environment. This can help you identify unusually long ones that - may indicate that attackers are executing commands on yout systems. how_to_implement: You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number @@ -39,18 +17,19 @@ how_to_implement: You must be ingesting endpoint data and populating the Endpoin a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459 -known_false_positives: '' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2019-05-08' -name: Baseline of Command Line Length - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as start_time + max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest + Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search + user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| + eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel' +tags: + analytics_story: + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + - Suspicious Command-Line Executions + - Suspicious MSHTA Activity + - Unusual Processes + detections: + - Detect Prohibited Applications Spawning cmd.exe + - Unusually Long Command Line - MLTK diff --git a/baselines/train_dns_query_length.yml b/baselines/baseline_of_dns_query_length___mltk.yml similarity index 51% rename from baselines/train_dns_query_length.yml rename to baselines/baseline_of_dns_query_length___mltk.yml index 3a796e39b7..2cd47fc291 100644 --- a/baselines/train_dns_query_length.yml +++ b/baselines/baseline_of_dns_query_length___mltk.yml @@ -1,31 +1,12 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -31d@d - latest_time: -1d@d - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query - DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | - eval query_length = len(query) | fit DensityFunction query_length by record_type - into dns_query_pdfmodel' -creation_date: '2019-05-08' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: Baseline of DNS Query Length - MLTK +id: c914844c-0ff5-4efc-8d44-c063443129ba +version: 1 +date: '2019-05-08' description: This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. -eli5: Create a machine-learning (ML) model to characterize the length of DNS requests - seen in your environment to help identify unusually long ones that may be indicative - of attacker infrastrucutre or the use of DNS as a command-and-control channel in - your environment. how_to_implement: To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any @@ -34,18 +15,15 @@ how_to_implement: To successfully implement this search, you will need to ensure period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -id: c914844c-0ff5-4efc-8d44-c063443129ba -known_false_positives: '' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2019-05-08' -name: Baseline of DNS Query Length - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution + by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` + | eval query_length = len(query) | fit DensityFunction query_length by record_type + into dns_query_pdfmodel' +tags: + analytics_story: + - Command and Control + - Hidden Cobra Malware + - Suspicious DNS Traffic + detections: + - DNS Query Length Outliers - MLTK diff --git a/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml b/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml new file mode 100644 index 0000000000..d5bdab4b81 --- /dev/null +++ b/baselines/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml @@ -0,0 +1,33 @@ +name: Baseline of Excessive AWS Instances Launched by User - MLTK +id: fa5634df-fb05-4b4b-aba0-6115138bb1ba +version: 1 +date: '2019-11-14' +description: This search is used to build a Machine Learning Toolkit (MLTK) model + for how many RunInstances users do in the environment. By default, the search uses + the last 90 days of data to build the model. The model created by this search is + then used in the corresponding detection search, which identifies subsequent outliers + in the number of RunInstances performed by a user in a small time window. +how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs.\ + + In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, + along with any required dependencies. Depending on the number of users in your environment, + you may also need to adjust the value for max_inputs in the MLTK settings for the + DensityFunction algorithm, then ensure that the search completes in a reasonable + timeframe. By default, the search builds the model using the past 30 days of data. + You can modify the search window to build the model over a longer period of time, + which may give you better results. You may also want to periodically re-run this + search to rebuild the model with the latest data.\ + + More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.' +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` + | bucket span=10m _time | stats count as instances_launched by _time src_user | + fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1' +tags: + analytics_story: + - Cloud Cryptomining + - Suspicious AWS EC2 Activities + detections: + - Abnormally High AWS Instances Launched by User - MLTK diff --git a/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml b/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml new file mode 100644 index 0000000000..d342dddd11 --- /dev/null +++ b/baselines/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml @@ -0,0 +1,33 @@ +name: Baseline of Excessive AWS Instances Terminated by User - MLTK +id: b28ed6de-e4ba-40f7-ae0a-93a088c774ab +version: 1 +date: '2019-11-14' +description: This search is used to build a Machine Learning Toolkit (MLTK) model + for how many TerminateInstances users do in the environment. By default, the search + uses the last 90 days of data to build the model. The model created by this search + is then used in the corresponding detection search, which identifies subsequent + outliers in the number of TerminateInstances performed by a user in a small time + window. +how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs.\ + + In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, + along with any required dependencies. Depending on the number of users in your environment, + you may also need to adjust the value for max_inputs in the MLTK settings for the + DensityFunction algorithm, then ensure that the search completes in a reasonable + timeframe. By default, the search builds the model using the past 30 days of data. + You can modify the search window to build the model over a longer period of time, + which may give you better results. You may also want to periodically re-run this + search to rebuild the model with the latest data.\ + + More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.' +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` + | bucket span=10m _time | stats count as instances_terminated by _time src_user + | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1' +tags: + analytics_story: + - Suspicious AWS EC2 Activities + detections: + - Abnormally High AWS Instances Terminated by User - MLTK diff --git a/baselines/baseline_of_network_acl_activity_by_arn.yml b/baselines/baseline_of_network_acl_activity_by_arn.yml new file mode 100644 index 0000000000..8d815ed92a --- /dev/null +++ b/baselines/baseline_of_network_acl_activity_by_arn.yml @@ -0,0 +1,23 @@ +name: Baseline of Network ACL Activity by ARN +id: fc0edd96-ff2b-4810-9f1f-63da3783fd63 +version: 1 +date: '2018-05-21' +description: This search establishes, on a per-hour basis, the average and the standard + deviation of the number of API calls that were related to network ACLs made by each + user. Also recorded is the number of data points for each user. This table is then + outputted to a lookup file to allow the detection search to operate quickly. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. +author: Bhavin Patel, Splunk +search: '`cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn + | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) + as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, + stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, + avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats + count' +tags: + analytics_story: + - AWS Network ACL Activity + detections: + - Detect Spike in Network ACL Activity diff --git a/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml new file mode 100644 index 0000000000..1b0075050d --- /dev/null +++ b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml @@ -0,0 +1,22 @@ +name: Baseline of S3 Bucket deletion activity by ARN +id: fc0edd96-ff2b-48b0-9f1f-63eq3783fd63 +version: 1 +date: '2018-07-17' +description: This search establishes, on a per-hour basis, the average and standard + deviation for the number of API calls related to deleting an S3 bucket by each user. + Also recorded is the number of data points for each user. This table is then outputted + to a lookup file to allow the detection search to operate quickly. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn + | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) + as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, + stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, + avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count' +tags: + analytics_story: + - Suspicious AWS S3 Activities + detections: + - Detect Spike in S3 Bucket deletion diff --git a/baselines/baseline_of_security_group_activity_by_arn.yml b/baselines/baseline_of_security_group_activity_by_arn.yml new file mode 100644 index 0000000000..1c0b705c71 --- /dev/null +++ b/baselines/baseline_of_security_group_activity_by_arn.yml @@ -0,0 +1,23 @@ +name: Baseline of Security Group Activity by ARN +id: fc0edd96-ff2b-48b0-9f1f-63da3783fd63 +version: 1 +date: '2018-04-17' +description: This search establishes, on a per-hour basis, the average and the standard + deviation for the number of API calls related to security groups made by each user. + Also recorded is the number of data points for each user. This table is then outputted + to a lookup file to allow the detection search to operate quickly. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. +author: Bhavin Patel, Splunk +search: '`cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn + | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) + as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, + stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, + avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats + count' +tags: + analytics_story: + - AWS User Monitoring + detections: + - Detect Spike in Security Group Activity diff --git a/baselines/train_smb_traffic_spike.yml b/baselines/baseline_of_smb_traffic___mltk.yml similarity index 53% rename from baselines/train_smb_traffic_spike.yml rename to baselines/baseline_of_smb_traffic___mltk.yml index 0217269002..e5b850a63c 100644 --- a/baselines/train_smb_traffic_spike.yml +++ b/baselines/baseline_of_smb_traffic___mltk.yml @@ -1,35 +1,13 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -31d@d - latest_time: -1d@d - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 - OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src - | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") - | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" - into smb_pdfmodel' -creation_date: '2019-05-08' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Splunk Stream - - Bro +name: Baseline of SMB Traffic - MLTK +id: df98763b-0b08-4281-8ef9-08db7ac572a9 +version: 1 +date: '2019-05-08' description: This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. -eli5: Create a machine-learning (ML) model to characterize the number of SMB connections - observed in your environment. This may help identify spikes in SMB traffic that - may be indicative of attackers scanning or attempting to propagate to other systems - in your environment. By default, this model is built over 30 days of data and profiles - the number of SMB connections in your environment by the hour of day/day of week - that the connections occur. how_to_implement: You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, @@ -43,18 +21,20 @@ how_to_implement: You must be ingesting network traffic and populating the Netwo which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -id: df98763b-0b08-4281-8ef9-08db7ac572a9 -known_false_positives: '' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2019-05-08' -name: Baseline of SMB Traffic - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic + where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb + by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval + DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction + count by "HourOfDay,DayOfWeek" into smb_pdfmodel' +tags: + analytics_story: + - DHS Report TA18-074A + - Disabling Security Tools + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Netsh Abuse + - Ransomware + detections: + - Processes launching netsh + - SMB Traffic Spike - MLTK diff --git a/baselines/baseline_s3_deletion_by_arn.yml b/baselines/baseline_s3_deletion_by_arn.yml deleted file mode 100644 index bf92bae087..0000000000 --- a/baselines/baseline_s3_deletion_by_arn.yml +++ /dev/null @@ -1,48 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn - | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) - as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, - stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, - avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count -creation_date: '2018-07-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search establishes, on a per-hour basis, the average and standard - deviation for the number of API calls related to deleting an S3 bucket by each user. - Also recorded is the number of data points for each user. This table is then outputted - to a lookup file to allow the detection search to operate quickly. -eli5: Use this search to create a baseline for API calls related to deleting an S3 - bucket, grouped by the users who initiated this activity. It returns all logged - API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated - each call. Next, it calculates the number of API calls made per ARN per hour. For - each ARN, it calculates the average and standard deviation of this count on a per-hour - basis. It also includes the number of data points for each ARN. This table is stored - in a lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. -id: fc0edd96-ff2b-48b0-9f1f-63eq3783fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-07-17' -name: Baseline of S3 Bucket deletion activity by ARN -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/baseline_security_group_activity_by_arn.yml b/baselines/baseline_security_group_activity_by_arn.yml deleted file mode 100644 index aca2ef9bb2..0000000000 --- a/baselines/baseline_security_group_activity_by_arn.yml +++ /dev/null @@ -1,49 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `security_group_api_calls` | spath output=arn path=userIdentity.arn - | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) - as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, - stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, - avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | - stats count -creation_date: '2018-04-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search establishes, on a per-hour basis, the average and the standard - deviation for the number of API calls related to security groups made by each user. - Also recorded is the number of data points for each user. This table is then outputted - to a lookup file to allow the detection search to operate quickly. -eli5: Use this search to create a baseline for API calls related to security groups - by the users who initiated this activity. It returns all logged API calls for all - security-group-related activity, pulls out the ARN that initiated each call, and - collects the `eventNames` in one-hour groupings. Next, it calculates the number - of API calls made per ARN per hour. For each ARN, it calculates the average and - standard deviation of this count on a per-hour basis. It also includes the number - of data points for each ARN. This table is stored in a lookup file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -id: fc0edd96-ff2b-48b0-9f1f-63da3783fd63 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-04-17' -name: Baseline of Security Group Activity by ARN -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/brand_abuse_dnstwist_domains.yml b/baselines/brand_abuse_dnstwist_domains.yml deleted file mode 100644 index 1f1d235a65..0000000000 --- a/baselines/brand_abuse_dnstwist_domains.yml +++ /dev/null @@ -1,43 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" - | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count' -creation_date: '2017-06-01' -data_metadata: - data_source: - - Splunk Enterprise Security Content Update - providing_technologies: - - Splunk Enterprise -description: This search creates permutations of your existing domains, removes the - valid domain names and stores them in a specified lookup file so they can be checked - for in the associated detection searches. -eli5: This search starts with the dnstwist command consuming domains from a file called - domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\_abuse=true - term to each permutation, removes all the valid domain names and stores all that - information into a lookup file that is used in the associated detection search. - Alternatively domain dnstwist permutations can be calculated from domains in the - `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located - in **Splunk\_SA\_CIM** using argument `populate_from_cim=true`. Also an individual - domain can be passed using argument `domain=` -how_to_implement: To successfully implement this search you need to update the file - called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` - and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. -id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f -known_false_positives: '' -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2018-10-08' -name: DNSTwist Domain Names -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/baselines/count_of_assets_by_category.yml b/baselines/count_of_assets_by_category.yml new file mode 100644 index 0000000000..66fd875219 --- /dev/null +++ b/baselines/count_of_assets_by_category.yml @@ -0,0 +1,19 @@ +name: Count of assets by category +id: dcfd6b40-42f9-469d-a433-2e53f7489ff9 +version: 1 +date: '2017-09-13' +description: This search shows you every asset category you have and the assets that + belong to those categories. +how_to_implement: To successfully implement this search you must first leverage the + Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv + file which should then be mapped to the Identity_Management data model. The Identity_Management + data model will contain a list of known authorized company assets. Ensure that all + inventoried systems are constantly vetted and updated. +author: Bhavin Patel, Splunk +search: '| from datamodel Identity_Management.All_Assets | stats count values(nt_host) + by category | sort -count' +tags: + analytics_story: + - Asset Tracking + detections: + - Detect Unauthorized Assets by MAC address diff --git a/baselines/count_of_unique_ips_connecting_to_ports.yml b/baselines/count_of_unique_ips_connecting_to_ports.yml new file mode 100644 index 0000000000..6b36cfaeae --- /dev/null +++ b/baselines/count_of_unique_ips_connecting_to_ports.yml @@ -0,0 +1,13 @@ +name: Count of Unique IPs Connecting to Ports +id: 9f3bae5a-9fe3-49df-8c84-5edc51d84b7f +version: 1 +date: '2017-09-13' +description: The search counts the number of times a connection was observed to each + destination port, and the number of unique source IPs connecting to them. +how_to_implement: To successfully implement this search, you must be ingesting network + traffic, and populating the Network_Traffic data model. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts + from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` + | sort - count' +tags: {} diff --git a/baselines/create_a_list_of_approved_aws_service_accounts.yml b/baselines/create_a_list_of_approved_aws_service_accounts.yml new file mode 100644 index 0000000000..29516b810e --- /dev/null +++ b/baselines/create_a_list_of_approved_aws_service_accounts.yml @@ -0,0 +1,21 @@ +name: Create a list of approved AWS service accounts +id: fc0edc95-ff2b-48b1-5f6f-63ga3789fd43 +version: 2 +date: '2018-12-03' +description: This search looks for successful API activity in CloudTrail within the + last 30 days, filters out known users from the identity table, and outputs values + of users into `aws_service_accounts.csv` lookup file. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Please validate the service account entires in `aws_service_accounts.csv`, + which is a lookup file created as a result of running this support search. Please + remove the entries of service accounts that are not legitimate. +author: Bhavin Patel, Splunk +search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT + [inputlookup identity_lookup_expanded | fields identity] | stats count by identity + | table identity | outputlookup aws_service_accounts | stats count' +tags: + analytics_story: + - AWS User Monitoring + detections: + - Detect AWS API Activities From Unapproved Accounts diff --git a/baselines/discover_dns_records.yml b/baselines/discover_dns_records.yml index 449dc34241..0e0e13b35b 100644 --- a/baselines/discover_dns_records.yml +++ b/baselines/discover_dns_records.yml @@ -1,52 +1,26 @@ -baseline: - splunk: - schedule: - cron_schedule: 0 0 1 * * - earliest_time: -30d@d - latest_time: -10m@m - search: '| inputlookup cim_corporate_email_domains.csv | inputlookup append=T - cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | - eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` - count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution - where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query - | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] - | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer - | outputlookup createinapp=true discovered_dns_records.csv' -creation_date: '2019-02-14' -data_metadata: - data_models: - - Network_Resolution - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro +name: Discover DNS records +id: c096f721-8842-42ce-bfc7-74bd8c72b7c3 +version: 1 +date: '2019-02-14' description: The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup -eli5: Discover the DNS records and their answers for domains owned by the company - using network traffic events. The discovered events are exported as a lookup named - `discovered_dns_records.csv` -entities: - - query - - answer how_to_implement: To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -id: c096f721-8842-42ce-bfc7-74bd8c72b7c3 -known_false_positives: 'Please vet the lookup created by this baseline search ' -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2019-02-14' -name: Discover DNS records -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -spec_version: 2 -type: splunk -version: '1.0' +author: Jose Hernandez, Splunk +search: '| inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv + | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, + "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) + as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE + DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | + where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv + delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer + | outputlookup createinapp=true discovered_dns_records.csv' +tags: + analytics_story: + - DNS Hijacking + detections: + - DNS record changed diff --git a/baselines/dnstwist_domain_names.yml b/baselines/dnstwist_domain_names.yml new file mode 100644 index 0000000000..dde08528b5 --- /dev/null +++ b/baselines/dnstwist_domain_names.yml @@ -0,0 +1,21 @@ +name: DNSTwist Domain Names +id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f +version: 2 +date: '2018-10-08' +description: This search creates permutations of your existing domains, removes the + valid domain names and stores them in a specified lookup file so they can be checked + for in the associated detection searches. +how_to_implement: To successfully implement this search you need to update the file + called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` + and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. +author: David Dorsey, Splunk +search: '| dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" + | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count' +tags: + analytics_story: + - Brand Monitoring + - Suspicious Emails + detections: + - Monitor Email For Brand Abuse + - Monitor DNS For Brand Abuse + - Monitor Web Traffic For Brand Abuse diff --git a/baselines/identify_ports_on_network.yml b/baselines/identify_ports_on_network.yml deleted file mode 100644 index fd19d7fe50..0000000000 --- a/baselines/identify_ports_on_network.yml +++ /dev/null @@ -1,39 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts - from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` - | sort - count' -creation_date: '2017-06-24' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro -description: The search counts the number of times a connection was observed to each - destination port, and the number of unique source IPs connecting to them. -eli5: For each port being accessed on the network, this search gives the total number - of connections observed, and the number of unique IP addresses making those connections. -how_to_implement: To successfully implement this search, you must be ingesting network - traffic, and populating the Network_Traffic data model. -id: 9f3bae5a-9fe3-49df-8c84-5edc51d84b7f -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-13' -name: Count of Unique IPs Connecting to Ports -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/identify_systems_creating_rdp_traffic.yml b/baselines/identify_systems_creating_rdp_traffic.yml deleted file mode 100644 index 7a8029351b..0000000000 --- a/baselines/identify_systems_creating_rdp_traffic.yml +++ /dev/null @@ -1,38 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 - by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count' -creation_date: '2017-04-24' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro -description: This search counts the numbers of times the system has generated remote - desktop traffic. -eli5: This search counts the numbers of times the system has tried to connect to another - system on TCP/3389, the default port used for RDP traffic. -how_to_implement: To successfully implement this search, you must ingest network traffic - and populate the Network_Traffic data model. -id: 5cdda34f-4caf-4128-a713-0837fc48b67a -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-15' -name: Identify Systems Creating Remote Desktop Traffic -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/identify_systems_creating_remote_desktop_traffic.yml b/baselines/identify_systems_creating_remote_desktop_traffic.yml new file mode 100644 index 0000000000..8ab2a53cec --- /dev/null +++ b/baselines/identify_systems_creating_remote_desktop_traffic.yml @@ -0,0 +1,13 @@ +name: Identify Systems Creating Remote Desktop Traffic +id: 5cdda34f-4caf-4128-a713-0837fc48b67a +version: 1 +date: '2017-09-15' +description: This search counts the numbers of times the system has generated remote + desktop traffic. +how_to_implement: To successfully implement this search, you must ingest network traffic + and populate the Network_Traffic data model. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic + where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` + | sort - count' +tags: {} diff --git a/baselines/identify_systems_receiving_rdp_traffic.yml b/baselines/identify_systems_receiving_rdp_traffic.yml deleted file mode 100644 index b838cf87be..0000000000 --- a/baselines/identify_systems_receiving_rdp_traffic.yml +++ /dev/null @@ -1,39 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 - by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count' -creation_date: '2017-04-24' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro -description: This search counts the numbers of times the system has created remote - desktop traffic -eli5: This search counts the numbers of times the system has received a connection - to TCP/ 3389, the default port used for RDP traffic. -how_to_implement: To successfully implement this search you must ingest network traffic - and populate the Network_Traffic data model. If a system receives a lot of remote - desktop traffic, you can apply the category common_rdp_destination to it. -id: baaeea15-fe8a-4090-92c2-5b60943bb608 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-15' -name: Identify Systems Receiving Remote Desktop Traffic -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/identify_systems_receiving_remote_desktop_traffic.yml b/baselines/identify_systems_receiving_remote_desktop_traffic.yml new file mode 100644 index 0000000000..db61f6cb75 --- /dev/null +++ b/baselines/identify_systems_receiving_remote_desktop_traffic.yml @@ -0,0 +1,14 @@ +name: Identify Systems Receiving Remote Desktop Traffic +id: baaeea15-fe8a-4090-92c2-5b60943bb608 +version: 1 +date: '2017-09-15' +description: This search counts the numbers of times the system has created remote + desktop traffic +how_to_implement: To successfully implement this search you must ingest network traffic + and populate the Network_Traffic data model. If a system receives a lot of remote + desktop traffic, you can apply the category common_rdp_destination to it. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic + where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` + | sort - count' +tags: {} diff --git a/baselines/identify_systems_using_remote_desktop.yml b/baselines/identify_systems_using_remote_desktop.yml index f58438864d..a81c57ea04 100644 --- a/baselines/identify_systems_using_remote_desktop.yml +++ b/baselines/identify_systems_using_remote_desktop.yml @@ -1,43 +1,13 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where - Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name - | `drop_dm_object_name(Processes)` | sort - count' -creation_date: '2017-04-18' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Identify Systems Using Remote Desktop +id: 063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8 +version: 1 +date: '2019-04-01' description: This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. -eli5: This search counts the numbers of times the remote desktop process, mstsc.exe, - has run on each system. It does this by looking for the process name in the Endpoint - data model. how_to_implement: To successfully implement this search you must be ingesting endpoint data that records process activity. -id: 063dfe9f-b1d7-4254-a16d-1e2e7eadd6a8 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-04-01' -name: Identify Systems Using Remote Desktop -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes + where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name + | `drop_dm_object_name(Processes)` | sort - count' +tags: {} diff --git a/baselines/monitor_successful_backups.yml b/baselines/monitor_successful_backups.yml index 2609115c6f..720e6c10fe 100644 --- a/baselines/monitor_successful_backups.yml +++ b/baselines/monitor_successful_backups.yml @@ -1,39 +1,18 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." - | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) - as dest by _time, MESSAGE -creation_date: '2017-08-24' -data_metadata: - data_source: - - Backup Logs - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup +name: Monitor Successful Backups +id: b4d0dfb2-2195-4f6e-93a3-48468ed9734e +version: 1 +date: '2017-09-12' description: This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. -eli5: This search gives you the count and the hostname of all the systems that had - a successful backup each day. how_to_implement: To successfully implement this search you must be ingesting your backup logs. -id: b4d0dfb2-2195-4f6e-93a3-48468ed9734e -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-12' -name: Monitor Successful Backups -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`netbackup` "Disk/Partition backup completed successfully." | bucket _time + span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, + MESSAGE' +tags: + analytics_story: + - Monitor Backup Solution + detections: + - Unsuccessful Netbackup backups diff --git a/baselines/monitor_successful_windows_updates.yml b/baselines/monitor_successful_windows_updates.yml deleted file mode 100644 index 1c0b69c009..0000000000 --- a/baselines/monitor_successful_windows_updates.yml +++ /dev/null @@ -1,38 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates - where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed - by _time span=1d' -creation_date: '2017-08-24' -data_metadata: - data_models: - - Updates - data_source: - - Windows Updates - providing_technologies: - - Microsoft Windows -description: This search is intended to give you a feel for how often successful Windows - updates are applied in your environments. Fluctuations in these numbers will allow - you to determine when you should be concerned. -eli5: This search gives you the count and name of all the systems that had a successful - update applied each day -how_to_implement: You must be ingesting your Windows Update Logs -id: 6a80535c-86a6-4b54-894c-4b446d0c701d -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-14' -name: Windows Updates Install Successes -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/monitor_unsuccessful_backups.yml b/baselines/monitor_unsuccessful_backups.yml index 9966a9426a..7b82a8a17c 100644 --- a/baselines/monitor_unsuccessful_backups.yml +++ b/baselines/monitor_unsuccessful_backups.yml @@ -1,39 +1,17 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype="netbackup_logs" "An error occurred, failed to backup." | bucket - _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest - by _time, MESSAGE -creation_date: '2017-08-24' -data_metadata: - data_source: - - Backup Logs - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup +name: Monitor Unsuccessful Backups +id: b2178fed-592f-492b-b851-74161678aa56 +version: 1 +date: '2017-09-12' description: This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. -eli5: This search gives you the count and hostname of all the systems that had a backup - failure each day how_to_implement: To successfully implement this search you must be ingesting your backup logs. -id: b2178fed-592f-492b-b851-74161678aa56 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-12' -name: Monitor Unsuccessful Backups -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`netbackup` "An error occurred, failed to backup." | bucket _time span=1d + | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE' +tags: + analytics_story: + - Monitor Backup Solution + detections: + - Unsuccessful Netbackup backups diff --git a/baselines/monitor_unsuccessful_windows_updates.yml b/baselines/monitor_unsuccessful_windows_updates.yml deleted file mode 100644 index a1b734bf0e..0000000000 --- a/baselines/monitor_unsuccessful_windows_updates.yml +++ /dev/null @@ -1,38 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates - where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure - by _time span=1d' -creation_date: '2017-08-24' -data_metadata: - data_models: - - Updates - data_source: - - Windows Updates - providing_technologies: - - Microsoft Windows -description: This search is intended to give you a feel for how often Windows updates - fail to install in your environment. Fluctuations in these numbers will allow you - to determine when you should be concerned. -eli5: This search gives you the count of the number of systems that attempted and - failed to install a Windows update each day. -how_to_implement: You must be ingesting your Windows Update Logs -id: 6a4dbd1b-4502-4a11-943a-82b5ae7a42d7 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-14' -name: Windows Updates Install Failures -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml b/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml new file mode 100644 index 0000000000..ac87b6efbc --- /dev/null +++ b/baselines/previously_seen_api_call_per_user_roles_in_cloudtrail.yml @@ -0,0 +1,22 @@ +name: Previously seen API call per user roles in CloudTrail +id: fc0edc95-fq2c-48b0-9f6f-63da3289fd03 +version: 1 +date: '2018-04-16' +description: This search looks for successful API calls made by different user roles, + then creates a baseline of the earliest and latest times we have encountered this + user role. It also returns the name of the API call in our dataset--grouped by user + role and name of the API call--that occurred within the last 30 days. In this support + search, we are only looking for events where the user identity is Assumed Role. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, + which is a lookup file created as a result of running this support search. +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole + | stats earliest(_time) as earliest latest(_time) as latest by userName eventName + | outputlookup previously_seen_api_calls_from_user_roles | stats count' +tags: + analytics_story: + - AWS User Monitoring + detections: + - Detect new API calls from user roles diff --git a/baselines/previously_seen_aws_cross_account_activity.yml b/baselines/previously_seen_aws_cross_account_activity.yml index 07db734f93..bd658f15b5 100644 --- a/baselines/previously_seen_aws_cross_account_activity.yml +++ b/baselines/previously_seen_aws_cross_account_activity.yml @@ -1,45 +1,22 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId - path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId - | search requestingAccountId=* | where requestingAccountId!=requestedAccountId - | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, - requestedAccountId | outputlookup previously_seen_aws_cross_account_activity - | stats count -creation_date: '2018-06-04' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: Previously Seen AWS Cross Account Activity +id: 1cc22b09-c867-416e-a511-cb36ac44aee2 +version: 1 +date: '2018-06-04' description: This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. -eli5: In this support search, we look for **AssumeRole** events where the requesting - account is different from the requested account. The first and last times these - events are seen are written to a lookup file. how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -id: 1cc22b09-c867-416e-a511-cb36ac44aee2 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-04' -name: Previously Seen AWS Cross Account Activity -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId + | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* + | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime + latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup + previously_seen_aws_cross_account_activity | stats count' +tags: + analytics_story: + - AWS Cross Account Activity + detections: + - AWS Cross Account Activity From Previously Unseen Account diff --git a/baselines/previously_seen_aws_provisioning_activity_sources.yml b/baselines/previously_seen_aws_provisioning_activity_sources.yml new file mode 100644 index 0000000000..c4eec20fd3 --- /dev/null +++ b/baselines/previously_seen_aws_provisioning_activity_sources.yml @@ -0,0 +1,23 @@ +name: Previously Seen AWS Provisioning Activity Sources +id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee +version: 1 +date: '2018-03-16' +description: This search builds a table of the first and last times seen for every + IP address (along with its physical location) previously associated with cloud-provisioning + activity. This is broadly defined as any event that runs or creates something. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress + | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, + City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv + | stats count' +tags: + analytics_story: + - AWS Suspicious Provisioning Activities + detections: + - AWS Cloud Provisioning From Previously Unseen IP Address + - AWS Cloud Provisioning From Previously Unseen City + - AWS Cloud Provisioning From Previously Unseen Country + - AWS Cloud Provisioning From Previously Unseen Region diff --git a/baselines/previously_seen_aws_regions.yml b/baselines/previously_seen_aws_regions.yml new file mode 100644 index 0000000000..2e9fd26e2c --- /dev/null +++ b/baselines/previously_seen_aws_regions.yml @@ -0,0 +1,20 @@ +name: Previously Seen AWS Regions +id: fc0edc95-ff2b-48b0-9f6f-63da3789fd63 +version: 1 +date: '2018-01-08' +description: This search looks for CloudTrail events where an AWS instance is started + and creates a baseline of most recent time (latest) and the first time (earliest) + we've seen this region in our dataset grouped by the value awsRegion for the last + 30 days +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +search: '`cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time) + as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count' +tags: + analytics_story: + - AWS Cryptomining + - Suspicious AWS EC2 Activities + detections: + - EC2 Instance Started In Previously Unseen Region diff --git a/baselines/previously_seen_aws_users.yml b/baselines/previously_seen_aws_users.yml deleted file mode 100644 index 06734edecd..0000000000 --- a/baselines/previously_seen_aws_users.yml +++ /dev/null @@ -1,56 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: 0 0 1 * * - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn - as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region - LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime - by user src City Region Country | outputlookup previously_seen_users_console_logins.csv - | stats count -creation_date: '2018-02-23' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user logs into the console, - then creates a baseline of the latest and earliest times, City, Region, and Country - we have encountered this user in our dataset, grouped by ARN, within the last 30 - days. -eli5: In this support search, we look for console login events by a particular user - and create an initial baseline cache for the previous 30 days, including the earliest - and latest times, City, Region, and Country a particular user ARN is seen in our - dataset, grouped by the ARN value. In cases where City and Region cannot be determined, - the source IP address is substituted for these values. -entities: - - user - - src -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, - which is a lookup file created as a result of running this support search. -id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 -known_false_positives: n/a -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2018-04-30' -name: Previously seen users in CloudTrail -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -spec_version: 2 -type: splunk -version: '1.1' diff --git a/baselines/previously_seen_cloud_compute_creations_by_user.yml b/baselines/previously_seen_cloud_compute_creations_by_user.yml index 819fb144a1..abc0c45380 100644 --- a/baselines/previously_seen_cloud_compute_creations_by_user.yml +++ b/baselines/previously_seen_cloud_compute_creations_by_user.yml @@ -1,45 +1,18 @@ -baseline: - splunk: - lookups: - - previously_seen_cloud_compute_creations_by_user - macros: - - previously_seen_cloud_compute_creations_by_user_input_filter - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from - datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_creations_by_user_input_filter` - by Compute.src_user | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_creations_by_user - | stats count' -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP +name: Previously Seen Cloud Compute Creations By User +id: 9fa1c205-4e08-4681-bb1b-d0943e734b85 +version: 1 +date: '2018-03-15' description: This search builds a table of previously seen users that have launched a cloud compute instance. -eli5: In this support search, we create a table of the earliest and latest time for - each user that has created a cloud compute instance. how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -id: 9fa1c205-4e08-4681-bb1b-d0943e734b85 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-15' -name: Previously Seen Cloud Compute Creations By User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute + where Compute.action=run `previously_seen_cloud_compute_creations_by_user_input_filter` + by Compute.src_user | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_creations_by_user + | stats count' +tags: + analytics_story: + - Cloud Cryptomining + detections: + - Cloud Compute Instance Created By Previously Unseen User diff --git a/baselines/previously_seen_cloud_compute_images.yml b/baselines/previously_seen_cloud_compute_images.yml index 85323c89f9..a0f152bff6 100644 --- a/baselines/previously_seen_cloud_compute_images.yml +++ b/baselines/previously_seen_cloud_compute_images.yml @@ -1,45 +1,18 @@ -baseline: - splunk: - lookups: - - previously_seen_cloud_compute_images - macros: - - previously_seen_cloud_compute_image_input_filter - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from - datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` - by Compute.image_id | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_images - | stats count' -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP +name: Previously Seen Cloud Compute Images +id: 3782ad10-5ce2-46e2-b9c4-1de9ecd3aecc +version: 1 +date: '2018-03-12' description: This search builds a table of previously seen images used to launch cloud compute instances -eli5: In this support search, we create a table of the earliest and latest time for - each image id that has been seen. This table is then outputted to a csv file. how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -id: 3782ad10-5ce2-46e2-b9c4-1de9ecd3aecc -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-12' -name: Previously Seen Cloud Compute Images -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute + where Compute.action=run `previously_seen_cloud_compute_image_input_filter` by Compute.image_id + | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_images + | stats count' +tags: + analytics_story: + - Cloud Cryptomining + detections: + - Cloud Compute Instance Created With Previously Unseen Image diff --git a/baselines/previously_seen_cloud_compute_instance_types.yml b/baselines/previously_seen_cloud_compute_instance_types.yml index 5d971aafcf..8cd2e0d610 100644 --- a/baselines/previously_seen_cloud_compute_instance_types.yml +++ b/baselines/previously_seen_cloud_compute_instance_types.yml @@ -1,48 +1,18 @@ -baseline: - splunk: - lookups: - - previously_seen_cloud_compute_instance_types - macros: - - previously_seen_cloud_compute_instance_types_input_filter - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from - datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_instance_types_input_filter` - by Compute.instance_type | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_instance_types - | stats count' -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP +name: Previously Seen Cloud Compute Instance Types +id: 0ef13d46-164e-4cf5-816e-b3c0df170d00 +version: 1 +date: '2019-10-03' description: This search builds a table of previously seen cloud compute instance types -eli5: In this support search, we create a table of the first time `firstTime` and - most recent time `lastTime` that the compute type has been seen in our dataset. - We only look for those events where an instance has been created. All of these entries - will be added to the `previously_seen_cloud_compute_instance_types` lookup file, - which will act as a baseline for detections. how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -id: 0ef13d46-164e-4cf5-816e-b3c0df170d00 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-10-03' -name: Previously Seen Cloud Compute Instance Types -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute + where Compute.action=run `previously_seen_cloud_compute_instance_types_input_filter` + by Compute.instance_type | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_instance_types + | stats count' +tags: + analytics_story: + - Cloud Cryptomining + detections: + - Cloud Compute Instance Created With Previously Unseen Instance Type diff --git a/baselines/previously_seen_cloud_regions.yml b/baselines/previously_seen_cloud_regions.yml new file mode 100644 index 0000000000..82fa2c9c9c --- /dev/null +++ b/baselines/previously_seen_cloud_regions.yml @@ -0,0 +1,20 @@ +name: Previously Seen Cloud Regions +id: b5e232db-dec6-4db8-aaa1-dd5474521e40 +version: 1 +date: '2019-10-02' +description: This search looks for cloud compute events where a compute instance is + started and creates a baseline of most recent time, `lastTime` and the first time + `firstTime` we've seen this region in our dataset grouped by the region for the + last 30 days +how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs + and have the Security Research cloud data model installed. +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute + where Compute.action=start `previously_seen_cloud_regions_input_filter` by Compute.region + | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_regions + | stats count' +tags: + analytics_story: + - Cloud Cryptomining + detections: + - Cloud Compute Instance Started In Previously Unused Region diff --git a/baselines/previously_seen_cmd_arguments.yml b/baselines/previously_seen_cmd_arguments.yml deleted file mode 100644 index cf73b2ad66..0000000000 --- a/baselines/previously_seen_cmd_arguments.yml +++ /dev/null @@ -1,48 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* - /c *" by Processes.process | `drop_dm_object_name(Processes)`' -creation_date: '2018-04-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for command-line arguments where `cmd.exe /c` is used - to execute a program, then creates a baseline of the earliest and latest times we - have encountered this command-line argument in our dataset within the last 30 days. -eli5: In this support search, we look for command-line arguments using the parameter - `/c` to execute processes and create an initial baseline cache for the previous - 30 days. This will include the earliest and latest times a particular command-line - argument is seen in our dataset, grouped by the command-line value. -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must be ingesting - logs with both the process name and command line from your endpoints. The complete - process name with command-line arguments are mapped to the "process" field in the - Endpoint data model. -id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-03-01' -name: Previously seen command line arguments -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/baselines/previously_seen_command_line_arguments.yml b/baselines/previously_seen_command_line_arguments.yml new file mode 100644 index 0000000000..8b15521edb --- /dev/null +++ b/baselines/previously_seen_command_line_arguments.yml @@ -0,0 +1,30 @@ +name: Previously seen command line arguments +id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23 +version: 2 +date: '2019-03-01' +description: This search looks for command-line arguments where `cmd.exe /c` is used + to execute a program, then creates a baseline of the earliest and latest times we + have encountered this command-line argument in our dataset within the last 30 days. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must be ingesting + logs with both the process name and command line from your endpoints. The complete + process name with command-line arguments are mapped to the "process" field in the + Endpoint data model. +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe + AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)`' +tags: + analytics_story: + - DHS Report TA18-074A + - Disabling Security Tools + - Hidden Cobra Malware + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Suspicious Command-Line Executions + - Suspicious MSHTA Activity + detections: + - Detect Prohibited Applications Spawning cmd.exe + - Processes launching netsh + - First time seen command line argument diff --git a/baselines/previously_seen_ec2_amis.yml b/baselines/previously_seen_ec2_amis.yml index db54ee9d52..c0c7b98592 100644 --- a/baselines/previously_seen_ec2_amis.yml +++ b/baselines/previously_seen_ec2_amis.yml @@ -1,40 +1,18 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename - requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) - as firstTime latest(_time) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv - | stats count -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: Previously Seen EC2 AMIs +id: bb1bd99d-1e93-45f1-9571-cfed42d372b9 +version: 1 +date: '2018-03-12' description: This search builds a table of previously seen AMIs used to launch EC2 instances -eli5: In this support search, we create a table of the earliest and latest time that - a specific AMI ID has been seen. This table is then outputted to a csv file. how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -id: bb1bd99d-1e93-45f1-9571-cfed42d372b9 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-12' -name: Previously Seen EC2 AMIs -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId + as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID + | outputlookup previously_seen_ec2_amis.csv | stats count' +tags: + analytics_story: + - AWS Cryptomining + detections: + - EC2 Instance Started With Previously Unseen AMI diff --git a/baselines/previously_seen_ec2_instance_types.yml b/baselines/previously_seen_ec2_instance_types.yml index 3f958f4f8c..d5e4a6ca47 100644 --- a/baselines/previously_seen_ec2_instance_types.yml +++ b/baselines/previously_seen_ec2_instance_types.yml @@ -1,41 +1,18 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename - requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType - | stats earliest(_time) as earliest latest(_time) as latest by instanceType - | outputlookup previously_seen_ec2_instance_types.csv | stats count -creation_date: '2018-03-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: Previously Seen EC2 Instance Types +id: b8f029f2-65a6-4d76-be98-dad1c9d59c45 +version: 1 +date: '2018-03-08' description: This search builds a table of previously seen EC2 instance types -eli5: In this support search, we create a table of the earliest and latest time that - a specific EC2 instance type has been seen. The instanceType request field is not - required and defaults to m1.small, so any time this field is null, the search defaults - the field to m1.small. This table is then outputted to a csv file. how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -id: b8f029f2-65a6-4d76-be98-dad1c9d59c45 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-08' -name: Previously Seen EC2 Instance Types -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType + as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) + as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv + | stats count' +tags: + analytics_story: + - AWS Cryptomining + detections: + - EC2 Instance Started With Previously Unseen Instance Type diff --git a/baselines/previously_seen_ec2_launches_by_user.yml b/baselines/previously_seen_ec2_launches_by_user.yml index d419d4e79d..df480fa6f8 100644 --- a/baselines/previously_seen_ec2_launches_by_user.yml +++ b/baselines/previously_seen_ec2_launches_by_user.yml @@ -1,40 +1,19 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename - userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as - lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats - count -creation_date: '2018-03-15' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: Previously Seen EC2 Launches By User +id: 6c767ac0-0906-4355-9a83-927f5ee7bdad +version: 1 +date: '2018-03-15' description: This search builds a table of previously seen ARNs that have launched a EC2 instance. -eli5: In this support search, we create a table of the earliest and latest times that - an ARN has launched a EC2 instance. This table is then outputted to a csv file. how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -id: 6c767ac0-0906-4355-9a83-927f5ee7bdad -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-15' -name: Previously Seen EC2 Launches By User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success | rename userIdentity.arn + as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup + previously_seen_ec2_launches_by_user.csv | stats count' +tags: + analytics_story: + - AWS Cryptomining + - Suspicious AWS EC2 Activities + detections: + - EC2 Instance Started With Previously Unseen User diff --git a/baselines/previously_seen_ec2_modifications.yml b/baselines/previously_seen_ec2_modifications.yml deleted file mode 100644 index 0933421192..0000000000 --- a/baselines/previously_seen_ec2_modifications.yml +++ /dev/null @@ -1,42 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `ec2_modification_api_calls` errorCode=success | spath - output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) - as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user - | stats count -creation_date: '2018-04-05' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search builds a table of previously seen ARNs that have launched - a EC2 instance. -eli5: In this support search, we create a table of the earliest and latest times that - an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined - in the `ec2_modification_api_calls` macro for ease of use. This table is then outputted - to a file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail - inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -id: 4d69091b-d975-4267-85df-888bd41034eb -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-04-05' -name: Previously Seen EC2 Modifications By User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/previously_seen_ec2_modifications_by_user.yml b/baselines/previously_seen_ec2_modifications_by_user.yml new file mode 100644 index 0000000000..4b637f233c --- /dev/null +++ b/baselines/previously_seen_ec2_modifications_by_user.yml @@ -0,0 +1,18 @@ +name: Previously Seen EC2 Modifications By User +id: 4d69091b-d975-4267-85df-888bd41034eb +version: 1 +date: '2018-04-05' +description: This search builds a table of previously seen ARNs that have launched + a EC2 instance. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail + inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. +author: David Dorsey, Splunk +search: '`cloudtrail` `ec2_modification_api_calls` errorCode=success | spath output=arn + userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime + by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count' +tags: + analytics_story: + - Unusual AWS EC2 Modifications + detections: + - EC2 Instance Modified With Previously Unseen User diff --git a/baselines/previously_seen_provisioning_activity_src.yml b/baselines/previously_seen_provisioning_activity_src.yml deleted file mode 100644 index 8e1159a426..0000000000 --- a/baselines/previously_seen_provisioning_activity_src.yml +++ /dev/null @@ -1,43 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -90d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation - sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime - by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv - | stats count -creation_date: '2018-03-16' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search builds a table of the first and last times seen for every - IP address (along with its physical location) previously associated with cloud-provisioning - activity. This is broadly defined as any event that runs or creates something. -eli5: This search includes any event name that begins with "run" or "create," and - then determines the first and last time these events were seen for each IP address - that initiated the action. The search then consults a **GeoIP** database to determine - the physical location of this IP address. This table outputs to a file. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-16' -name: Previously Seen AWS Provisioning Activity Sources -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/previously_seen_running_windows_services.yml b/baselines/previously_seen_running_windows_services.yml new file mode 100644 index 0000000000..457c380a0f --- /dev/null +++ b/baselines/previously_seen_running_windows_services.yml @@ -0,0 +1,20 @@ +name: Previously Seen Running Windows Services +id: 64ce0ade-cb01-4678-bddd-d31c0b175394 +version: 2 +date: '2020-01-13' +description: This collects the services that have been started across your entire + enterprise. +how_to_implement: While this search does not require you to adhere to Splunk CIM, + you must be ingesting your Windows security-event logs for it to execute successfully. + Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. +author: David Dorsey, Splunk +search: '`wineventlog_system` signature_id=7036 | rename param1 as service_name | + rename param2 as action | search action="running" | stats earliest(_time) as firstTime, + latest(_time) as lastTime by service_name | outputlookup previously_seen_running_windows_services + | stats count' +tags: + analytics_story: + - Orangeworm Attack Group + - Windows Service Abuse + detections: + - First Time Seen Running Windows Service diff --git a/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml new file mode 100644 index 0000000000..595012b42e --- /dev/null +++ b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml @@ -0,0 +1,21 @@ +name: Previously seen S3 bucket access by remote IP +id: fc0edc15-fq2c-48b0-9f6f-63qa1281fd03 +version: 1 +date: '2018-06-28' +description: This search looks for successful access to S3 buckets from remote IP + addresses, then creates a baseline of the earliest and latest times we have encountered + this remote IP within the last 30 days. In this support search, we are only looking + for S3 access events where the HTTP response code from AWS is "200" +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs + inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, + which is a lookup file created as a result of running this support search. +author: Bhavin Patel, Splunk +search: '`aws_s3_accesslogs` http_status=200 | stats earliest(_time) as earliest + latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip + | stats count' +tags: + analytics_story: + - Suspicious AWS S3 Activities + detections: + - Detect S3 access from a new IP diff --git a/baselines/previously_seen_s3_remote_ip.yml b/baselines/previously_seen_s3_remote_ip.yml deleted file mode 100644 index a2df669af3..0000000000 --- a/baselines/previously_seen_s3_remote_ip.yml +++ /dev/null @@ -1,45 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) - as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup - previously_seen_S3_access_from_remote_ip | stats count -creation_date: '2018-06-28' -data_metadata: - data_source: - - AWS S3 Access logs - data_sourcetypes: - - aws:s3:accesslogs - providing_technologies: - - AWS -description: This search looks for successful access to S3 buckets from remote IP - addresses, then creates a baseline of the earliest and latest times we have encountered - this remote IP within the last 30 days. In this support search, we are only looking - for S3 access events where the HTTP response code from AWS is "200" -eli5: In this support search, we are looking for successful S3 bucket-access attempts - made from remote IPs. The intent is to create an initial baseline cache of remote - IP addresses per bucket name for the previous 30 days--including the earliest and - latest times seen in our dataset--grouped by the value of remote IP and the name - of the S3 bucket. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs - inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, - which is a lookup file created as a result of running this support search. -id: fc0edc15-fq2c-48b0-9f6f-63qa1281fd03 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-06-28' -name: Previously seen S3 bucket access by remote IP -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/previously_seen_user_roles.yml b/baselines/previously_seen_user_roles.yml deleted file mode 100644 index 35b0498244..0000000000 --- a/baselines/previously_seen_user_roles.yml +++ /dev/null @@ -1,47 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole - | stats earliest(_time) as earliest latest(_time) as latest by userName eventName - | outputlookup previously_seen_api_calls_from_user_roles | stats count -creation_date: '2018-04-01' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for successful API calls made by different user roles, - then creates a baseline of the earliest and latest times we have encountered this - user role. It also returns the name of the API call in our dataset--grouped by user - role and name of the API call--that occurred within the last 30 days. In this support - search, we are only looking for events where the user identity is Assumed Role. -eli5: In this support search, we are looking for successful API calls made by user - roles within your AWS infrastructure. The intent is to create an initial baseline - cache of names of the API calls per security role for the previous 30 days--including - the earliest and latest times seen in our dataset--grouped by the value of user - role and the name of the API call. It is also worth noting that the role of a particular - user is parsed as "userName" in the CloudTrail logs. -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, - which is a lookup file created as a result of running this support search. -id: fc0edc95-fq2c-48b0-9f6f-63da3289fd03 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-04-16' -name: Previously seen API call per user roles in CloudTrail -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/previously_seen_users_in_cloudtrail.yml b/baselines/previously_seen_users_in_cloudtrail.yml new file mode 100644 index 0000000000..0a75480cd8 --- /dev/null +++ b/baselines/previously_seen_users_in_cloudtrail.yml @@ -0,0 +1,25 @@ +name: Previously seen users in CloudTrail +id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events where a user logs into the console, + then creates a baseline of the latest and earliest times, City, Region, and Country + we have encountered this user in our dataset, grouped by ARN, within the last 30 + days. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, + which is a lookup file created as a result of running this support search. +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation + src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | + stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region + Country | outputlookup previously_seen_users_console_logins.csv | stats count' +tags: + analytics_story: + - Suspicious AWS Login Activities + detections: + - Detect AWS Console Login by User from New Country + - Detect AWS Console Login by User from New Region + - Detect AWS Console Login by User from New City + - Detect new user AWS Console Login diff --git a/baselines/previously_seen_windows_service_starts.yml b/baselines/previously_seen_windows_service_starts.yml deleted file mode 100644 index e3d9b75d56..0000000000 --- a/baselines/previously_seen_windows_service_starts.yml +++ /dev/null @@ -1,37 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -30d@d - latest_time: -10m@m - search: eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | outputlookup previously_seen_running_windows_services | stats count -creation_date: '2018-07-20' -data_metadata: - data_eventtypes: - - wineventlog_system - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This collects the services that have been started across your entire - enterprise. -eli5: In this support search, we look for Windows system-event code that indicates - a status change of a Windows service. In this specific log event, the `param1` field represents the - "service_name" and the `param2` represents the action/status of the service. This search will create a table of the first and last time as particular Windows service was seen to be in the `running` status. -how_to_implement: While this search does not require you to adhere to Splunk CIM, - you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -id: 64ce0ade-cb01-4678-bddd-d31c0b175394 -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-01-13' -name: Previously Seen Running Windows Services -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/baselines/systems_ready_for_spectre_meltdown_patch.yml b/baselines/systems_ready_for_spectre_meltdown_patch.yml deleted file mode 100644 index 71d85e9df2..0000000000 --- a/baselines/systems_ready_for_spectre_meltdown_patch.yml +++ /dev/null @@ -1,47 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: '' - earliest_time: -1d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry - AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") - by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, - All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")`' -creation_date: '2018-01-08' -data_metadata: - data_models: - - Change_Analysis - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Some AV applications can cause the Spectre/Meltdown patch for Windows - not to install successfully. This registry key is supposed to be created by the - AV engine when it has been patched to be able to handle the Windows patch. If this - key has been written, the system can then be patched for Spectre and Meltdown. -eli5: This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. - It will tell you when it was created and, if possible, what process created it. -how_to_implement: You need to be ingesting logs with both the process name and command-line - from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -id: fc0edc95-ff2b-48b0-9f6f-63da3789fd61 -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-01-08' -name: Systems Ready for Spectre-Meltdown Windows Patch -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml new file mode 100644 index 0000000000..5503ddeb98 --- /dev/null +++ b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml @@ -0,0 +1,23 @@ +name: Systems Ready for Spectre-Meltdown Windows Patch +id: fc0edc95-ff2b-48b0-9f6f-63da3789fd61 +version: 1 +date: '2018-01-08' +description: Some AV applications can cause the Spectre/Meltdown patch for Windows + not to install successfully. This registry key is supposed to be created by the + AV engine when it has been patched to be able to handle the Windows patch. If this + key has been written, the system can then be patched for Spectre and Meltdown. +how_to_implement: You need to be ingesting logs with both the process name and command-line + from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 + of the Sysmon TA. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry + AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") + by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, + All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + | `drop_dm_object_name("All_Changes")`' +tags: + analytics_story: + - Spectre And Meltdown Vulnerabilities + detections: + - Spectre and Meltdown Vulnerable Systems diff --git a/baselines/train_ec2_excessive_runinstances.yml b/baselines/train_ec2_excessive_runinstances.yml deleted file mode 100644 index 3ecfc9b732..0000000000 --- a/baselines/train_ec2_excessive_runinstances.yml +++ /dev/null @@ -1,60 +0,0 @@ -baseline: - splunk: - macros: - - ec2_excessive_runinstances_mltk_input_filter - schedule: - cron_schedule: '' - earliest_time: -91d@d - latest_time: -1d@d - search: >- - sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` - | bucket span=10m _time - | stats count as instances_launched by _time src_user - | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1 -creation_date: '2019-11-14' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search is used to build a Machine Learning Toolkit (MLTK) model - for how many RunInstances users do in the environment. By default, the search - uses the last 90 days of data to build the model. The model created by this search - is then used in the corresponding detection search, which identifies subsequent - outliers in the number of RunInstances performed by a user in a small time window. -eli5: Create a machine-learning (ML) model to establish a baseline for how many - RunInstances users do in the environment. This can help you identify excessive - numbers of RunInstances which may warrant further investigation to determine if there - is misuse or abuse. -how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs.\ - - In addition, you must have the Machine Learning Toolkit (MLTK) version - >= 4.2 installed, along with any required dependencies. Depending on the number - of users in your environment, you may also need to adjust the value for max_inputs - in the MLTK settings for the DensityFunction algorithm, then ensure that the search - completes in a reasonable timeframe. By default, the search builds the model using - the past 30 days of data. You can modify the search window to build the model over - a longer period of time, which may give you better results. You may also want to - periodically re-run this search to rebuild the model with the latest data.\ - - More information on the algorithm used in the search can be found at - `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.' -id: fa5634df-fb05-4b4b-aba0-6115138bb1ba -known_false_positives: '' -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2019-11-14' -name: Baseline of Excessive AWS Instances Launched by User - MLTK -original_authors: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/train_ec2_excessive_terminateinstances.yml b/baselines/train_ec2_excessive_terminateinstances.yml deleted file mode 100644 index d84c119e4d..0000000000 --- a/baselines/train_ec2_excessive_terminateinstances.yml +++ /dev/null @@ -1,60 +0,0 @@ -baseline: - splunk: - macros: - - ec2_excessive_terminateinstances_mltk_input_filter - schedule: - cron_schedule: '' - earliest_time: -91d@d - latest_time: -1d@d - search: >- - sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` - | bucket span=10m _time - | stats count as instances_terminated by _time src_user - | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1 -creation_date: '2019-11-14' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search is used to build a Machine Learning Toolkit (MLTK) model - for how many TerminateInstances users do in the environment. By default, the search - uses the last 90 days of data to build the model. The model created by this search - is then used in the corresponding detection search, which identifies subsequent - outliers in the number of TerminateInstances performed by a user in a small time window. -eli5: Create a machine-learning (ML) model to establish a baseline for how many - TerminateInstances users do in the environment. This can help you identify excessive - numbers of TerminateInstances which may warrant further investigation to determine if there - is misuse or abuse. -how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs.\ - - In addition, you must have the Machine Learning Toolkit (MLTK) version - >= 4.2 installed, along with any required dependencies. Depending on the number - of users in your environment, you may also need to adjust the value for max_inputs - in the MLTK settings for the DensityFunction algorithm, then ensure that the search - completes in a reasonable timeframe. By default, the search builds the model using - the past 30 days of data. You can modify the search window to build the model over - a longer period of time, which may give you better results. You may also want to - periodically re-run this search to rebuild the model with the latest data.\ - - More information on the algorithm used in the search can be found at - `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.' -id: b28ed6de-e4ba-40f7-ae0a-93a088c774ab -known_false_positives: '' -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2019-11-14' -name: Baseline of Excessive AWS Instances Terminated by User - MLTK -original_authors: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/update_previously_seen_aws_users.yml b/baselines/update_previously_seen_aws_users.yml deleted file mode 100644 index 527d79bbef..0000000000 --- a/baselines/update_previously_seen_aws_users.yml +++ /dev/null @@ -1,50 +0,0 @@ -baseline: - splunk: - schedule: - cron_schedule: 0 0 1 * * - earliest_time: -60m@m - latest_time: m@m - search: sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn - as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region - LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime - by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region - Country | outputlookup previously_seen_users_console_logins.csv -creation_date: '2019-04-25' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user logs into the console, - then updates the baseline of the latest and earliest times, City, Region, and Country - we have encountered this user in our dataset, grouped by ARN, within the last hour. -eli5: In this support search, we look for console login events by a particular user - to update the baseline cache of users/arns making the accesses, including the earliest - and latest times, City, Region, and Country a particular user ARN is seen in our - dataset, grouped by the ARN value. In cases where City and Region cannot be determined, - the source IP address is substituted for these values. -entities: - - user - - src -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, - which is a lookup file created as a result of running this support search. -id: 06c036e6-d6d7-4daa-bd76-411c3d356031 -known_false_positives: n/a -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2018-04-30' -name: Update previously seen users in CloudTrail -original_authors: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -spec_version: 2 -type: splunk -version: '1.0' diff --git a/baselines/update_previously_seen_users_in_cloudtrail.yml b/baselines/update_previously_seen_users_in_cloudtrail.yml new file mode 100644 index 0000000000..cc8a111d73 --- /dev/null +++ b/baselines/update_previously_seen_users_in_cloudtrail.yml @@ -0,0 +1,26 @@ +name: Update previously seen users in CloudTrail +id: 06c036e6-d6d7-4daa-bd76-411c3d356031 +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events where a user logs into the console, + then updates the baseline of the latest and earliest times, City, Region, and Country + we have encountered this user in our dataset, grouped by ARN, within the last hour. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, + which is a lookup file created as a result of running this support search. +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation + src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | + stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region + Country | inputlookup append=t previously_seen_users_console_logins.csv | stats + min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country + | outputlookup previously_seen_users_console_logins.csv' +tags: + analytics_story: + - Suspicious AWS Login Activities + detections: + - Detect AWS Console Login by User from New Country + - Detect AWS Console Login by User from New Region + - Detect AWS Console Login by User from New City + - Detect new user AWS Console Login diff --git a/baselines/windows_updates_install_failures.yml b/baselines/windows_updates_install_failures.yml new file mode 100644 index 0000000000..8fa8ad8415 --- /dev/null +++ b/baselines/windows_updates_install_failures.yml @@ -0,0 +1,13 @@ +name: Windows Updates Install Failures +id: 6a4dbd1b-4502-4a11-943a-82b5ae7a42d7 +version: 1 +date: '2017-09-14' +description: This search is intended to give you a feel for how often Windows updates + fail to install in your environment. Fluctuations in these numbers will allow you + to determine when you should be concerned. +how_to_implement: You must be ingesting your Windows Update Logs +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM + datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure + by _time span=1d' +tags: {} diff --git a/baselines/windows_updates_install_successes.yml b/baselines/windows_updates_install_successes.yml new file mode 100644 index 0000000000..0b30e84d42 --- /dev/null +++ b/baselines/windows_updates_install_successes.yml @@ -0,0 +1,13 @@ +name: Windows Updates Install Successes +id: 6a80535c-86a6-4b54-894c-4b446d0c701d +version: 1 +date: '2017-09-14' +description: This search is intended to give you a feel for how often successful Windows + updates are applied in your environments. Fluctuations in these numbers will allow + you to determine when you should be concerned. +how_to_implement: You must be ingesting your Windows Update Logs +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM + datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed + by _time span=1d' +tags: {} diff --git a/bin/converter_v2_v3.py b/bin/converter_v2_v3.py new file mode 100644 index 0000000000..6c0cba747c --- /dev/null +++ b/bin/converter_v2_v3.py @@ -0,0 +1,382 @@ +import os +import sys +import yaml +import glob +import re + +from collections import OrderedDict +from attackcti import attack_client + + +lift = attack_client() +all_enterprise = lift.get_enterprise(stix_format=False) + +def represent_ordereddict(dumper, data): + value = [] + + for item_key, item_value in data.items(): + node_key = dumper.represent_data(item_key) + node_value = dumper.represent_data(item_value) + + value.append((node_key, node_value)) + + return yaml.nodes.MappingNode(u'tag:yaml.org,2002:map', value) + + +def attack_lookup_id(inputs_array): + outputs = [] + for input in inputs_array: + for technique in all_enterprise['techniques']: + if technique['technique'].lower()==input.lower(): + outputs.append(technique['external_references'][0]['external_id']) + + return outputs + + +def remove_special_characters(input_str): + output_str = input_str.replace('.',' ').replace('/',' ').replace('(',' ').replace(')',' ').replace('&','and').replace('_',' ') + return output_str + +def generate_content(): + ## detections ## + detection_files = glob.glob("../security-content-tmp/detections/*.yml") + story_files = glob.glob("../security-content-tmp/stories/*.yml") + old_detections = [] + old_stories = [] + for detection_file in detection_files: + old_detections.append(load_file(detection_file)) + + for story_file in story_files: + old_stories.append(load_file(story_file)) + + det_sto = map_detection_to_stories(old_stories) + + print('## Detections ##') + for orig_dict in old_detections: + print(orig_dict['name']) + new_dict = {} + new_dict['name'] = remove_special_characters(orig_dict['name']) + new_dict['id'] = orig_dict['id'] + new_dict['version'] = int(float(orig_dict['version'])) + if 'modification_date' in orig_dict: + new_dict['date'] = orig_dict['modification_date'] + else: + new_dict['date'] = orig_dict['creation_date'] + new_dict['description'] = orig_dict['description'] + new_dict['how_to_implement'] = orig_dict['how_to_implement'] + new_dict['type'] = 'ESCU' + if 'references' in orig_dict: + new_dict['references'] = orig_dict['references'] + for author in orig_dict['original_authors']: + author_str = author['name'] + ', ' + author['company'] + ', ' + new_dict['author'] = author_str[:-2] + if 'splunk' in orig_dict['detect']: + new_dict['search'] = orig_dict['detect']['splunk']['correlation_rule']['search'] + elif 'uba' in orig_dict['detect']: + new_dict['search'] = orig_dict['detect']['uba']['correlation_rule']['search'] + + if not str('_filter') in new_dict['search']: + new_dict['search'] = new_dict['search'] + ' | `' + new_dict['name'].replace('-','_').replace(' ','_').lower() + '_filter`' + + if 'search' in new_dict: + new_dict['search'] = check_source_macro(new_dict['search']) + new_dict['search'] = change_filter_macro(new_dict) + + new_dict['known_false_positives'] = orig_dict['known_false_positives'] + tag_dict = {} + if orig_dict['id'] in det_sto: + tag_dict['analytics_story'] = list(det_sto[orig_dict['id']]) + if 'mitre_attack' in orig_dict['mappings']: + mitre_attack_id = attack_lookup_id(orig_dict['mappings']['mitre_attack']) + if len(mitre_attack_id)>0: + tag_dict['mitre_attack_id'] = attack_lookup_id(orig_dict['mappings']['mitre_attack']) + if 'kill_chain_phases' in orig_dict['mappings']: + tag_dict['kill_chain_phases'] = orig_dict['mappings']['kill_chain_phases'] + if 'cis20' in orig_dict['mappings']: + tag_dict['cis20'] = orig_dict['mappings']['cis20'] + if 'nist' in orig_dict['mappings']: + tag_dict['nist'] = orig_dict['mappings']['nist'] + if 'security_domain' in orig_dict: + tag_dict['security_domain'] = orig_dict['security_domain'] + if 'asset_type' in orig_dict: + tag_dict['asset_type'] = orig_dict['asset_type'] + new_dict['tags'] = tag_dict + ordered_new_dict = OrderedDict(new_dict.items()) + new_file_name = new_dict['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + with open('detections/' + new_file_name + '.yml', 'w+' ) as outfile: + yaml.dump( new_dict , outfile , default_flow_style=False, sort_keys=False) + + + ## baselines ## + baseline_files = glob.glob("../security-content-tmp/baselines/*.yml") + old_baselines = [] + for baseline_file in baseline_files: + old_baselines.append(load_file(baseline_file)) + + bas_det = map_baselines_to_detection(old_detections) + old_baselines = enrich_baselines_with_stories(old_baselines, bas_det, det_sto) + bas_det_name = map_baselines_to_detection_names(old_detections) + + print() + print('## Baselines ##') + for orig_dict in old_baselines: + print(orig_dict['name']) + new_dict = {} + new_dict['name'] = remove_special_characters(orig_dict['name']) + new_dict['id'] = orig_dict['id'] + new_dict['version'] = int(float(orig_dict['version'])) + if 'modification_date' in orig_dict: + new_dict['date'] = orig_dict['modification_date'] + else: + new_dict['date'] = orig_dict['creation_date'] + new_dict['description'] = orig_dict['description'] + new_dict['how_to_implement'] = orig_dict['how_to_implement'] + for author in orig_dict['original_authors']: + author_str = author['name'] + ', ' + author['company'] + ', ' + new_dict['author'] = author_str[:-2] + new_dict['search'] = orig_dict['baseline']['splunk']['search'] + + new_dict['search'] = check_source_macro(new_dict['search']) + + tag_dict = {} + if len(orig_dict['stories']) > 0: + tag_dict['analytics_story'] = list(orig_dict['stories']) + if orig_dict['id'] in bas_det_name: + tag_dict['detections'] = list(bas_det_name[orig_dict['id']]) + new_dict['tags'] = tag_dict + ordered_new_dict = OrderedDict(new_dict.items()) + new_file_name = new_dict['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + with open('baselines/' + new_file_name + '.yml', 'w+' ) as outfile: + yaml.dump( new_dict , outfile , default_flow_style=False, sort_keys=False) + + + ## stories ## + story_files = glob.glob("../security-content-tmp/stories/*.yml") + old_stories = [] + for story_file in story_files: + old_stories.append(load_file(story_file)) + + print() + print('## Stories ##') + for orig_dict in old_stories: + print(orig_dict['name']) + new_dict = {} + new_dict['name'] = remove_special_characters(orig_dict['name']) + new_dict['id'] = orig_dict['id'] + new_dict['version'] = int(float(orig_dict['version'])) + if 'modification_date' in orig_dict: + new_dict['date'] = orig_dict['modification_date'] + else: + new_dict['date'] = orig_dict['creation_date'] + new_dict['description'] = orig_dict['description'] + new_dict['narrative'] = orig_dict['narrative'] + for author in orig_dict['original_authors']: + author_str = author['name'] + ', ' + author['company'] + ', ' + new_dict['author'] = author_str[:-2] + new_dict['type'] = 'ESCU' + if 'references' in orig_dict: + new_dict['references'] = orig_dict['references'] + tag_dict = {} + tag_dict['analytics_story'] = remove_special_characters(orig_dict['name']) + tag_dict['usecase'] = orig_dict['usecase'] + tag_dict['category'] = orig_dict['category'] + new_dict['tags'] = tag_dict + ordered_new_dict = OrderedDict(new_dict.items()) + new_file_name = new_dict['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + with open('stories/' + new_file_name + '.yml', 'w+' ) as outfile: + yaml.dump( new_dict , outfile , default_flow_style=False, sort_keys=False) + + + ## response tasks ## + investigation_files = glob.glob("../security-content-tmp/investigations/*.yml") + old_investigations = [] + for investigation_file in investigation_files: + old_investigations.append(load_file(investigation_file)) + + map_inv_det = map_investigations_to_detection(old_detections) + + print() + print('## Response Tasks ##') + for orig_dict in old_investigations: + print(orig_dict['name']) + new_dict = {} + new_dict['name'] = remove_special_characters(orig_dict['name']) + new_dict['id'] = orig_dict['id'] + new_dict['version'] = int(float(orig_dict['version'])) + if 'modification_date' in orig_dict: + new_dict['date'] = orig_dict['modification_date'] + else: + new_dict['date'] = orig_dict['creation_date'] + new_dict['description'] = orig_dict['description'] + new_dict['how_to_implement'] = orig_dict['how_to_implement'] + for author in orig_dict['original_authors']: + author_str = author['name'] + ', ' + author['company'] + ', ' + new_dict['author'] = author_str[:-2] + if 'splunk' in orig_dict['investigate']: + new_dict['inputs'] = orig_dict['investigate']['splunk']['fields_required'] + new_dict['search'] = orig_dict['investigate']['splunk']['search'] + new_dict = change_response_task_variable(new_dict) + # elif 'phantom' in orig_dict['investigate']: + # phantom_dict = {} + # phantom_dict['name'] = orig_dict['investigate']['phantom']['playbook_name'] + # phantom_dict['url_json'] = 'todo' + # phantom_dict['url_py'] = 'todo' + # new_dict['playbook'] = phantom_dict + else: + continue + stories = get_stories_for_investigations(map_inv_det, det_sto, orig_dict) + if len(stories) > 0: + tag_dict = {} + tag_dict['analytics_story'] = stories + new_dict['tags'] = tag_dict + ordered_new_dict = OrderedDict(new_dict.items()) + new_file_name = new_dict['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + with open('response_tasks/' + new_file_name + '.yml', 'w+' ) as outfile: + yaml.dump( new_dict , outfile , default_flow_style=False, sort_keys=False) + + + +def load_file(file_path): + with open(file_path, 'r') as stream: + try: + file = list(yaml.safe_load_all(stream))[0] + except yaml.YAMLError as exc: + print(exc) + sys.exit("ERROR: reading {0}".format(file_path)) + return file + +def map_investigations_to_detection(detections): + inv_det = {} + for detection in detections: + if 'investigations' in detection: + for investigation in detection['investigations']: + if not (investigation['id'] in inv_det): + inv_det[investigation['id']] = {detection['id']} + else: + inv_det[investigation['id']].add(detection['id']) + return inv_det + +def map_detection_to_stories(stories): + det_sto = {} + for story in stories: + if 'detections' in story: + for detection in story['detections']: + if not (detection['detection_id'] in det_sto): + det_sto[detection['detection_id']] = {remove_special_characters(story['name'])} + else: + det_sto[detection['detection_id']].add(remove_special_characters(story['name'])) + return det_sto + +def map_baselines_to_detection(detections): + bas_det = {} + for detection in detections: + if 'baselines' in detection: + for baseline in detection['baselines']: + if not (baseline['id'] in bas_det): + bas_det[baseline['id']] = {detection['id']} + else: + bas_det[baseline['id']].add(detection['id']) + return bas_det + +def map_baselines_to_detection_names(detections): + bas_det = {} + for detection in detections: + if 'baselines' in detection: + for baseline in detection['baselines']: + if not (baseline['id'] in bas_det): + bas_det[baseline['id']] = {detection['name']} + else: + bas_det[baseline['id']].add(detection['name']) + return bas_det + +def enrich_baselines_with_stories(baselines, map_bas_det, map_det_sto): + enriched_baselines = [] + for baseline in baselines: + stories_set = set() + if baseline['id'] in map_bas_det: + for baseline_id in map_bas_det[baseline['id']]: + if baseline_id in map_det_sto: + stories_set = stories_set | map_det_sto[baseline_id] + + baseline['stories'] = sorted(list(stories_set)) + enriched_baselines.append(baseline) + return enriched_baselines + + +def get_stories_for_investigations(map_inv_det, map_det_sto, investigation): + story_names = set() + if investigation['id'] in map_inv_det: + detections = map_inv_det[investigation['id']] + for detection in detections: + if detection in map_det_sto: + stories = map_det_sto[detection] + story_names = story_names | stories + + return sorted(list(story_names)) + + +def check_source_macro(search): + new_search = search + + mappings = {"aws:cloudtrail": "cloudtrail", + "netbackup_logs": "netbackup", + "okta_log": "okta", + "stream:http": "stream_http", + "google:gcp:pubsub:message": "google_gcp_pubsub_message", + "aws:s3:accesslogs": "aws_s3_accesslogs", + "aws:cloudwatchlogs:eks": "aws_cloudwatchlogs_eks", + "wineventlog_security": "wineventlog_security", + "XmlWinEventLog:Microsoft-Windows-Sysmon/Operational": "sysmon", + "wineventlog:microsoft-windows-wmi-activity/operational": "wmi", + "wineventlog_system": "wineventlog_system", + "aws:cloudwatchlogs:vpcflow": "cloudwatchlogs_vpcflow"} + + adjust_position = 0 + for match in re.finditer('(sourcetype\s?|index\s?|source\s?|eventtype\s?)=\s?([^\s)]*)',search): + if not match.group()=="source=pods" and not match.group()=="index=_internal" and not match.group()=="sourcetype=splunkd_ui_access" and not match.group()=="sourcetype=splunk_web_access": + content_match = match.group(2) + if content_match.startswith('"'): + content_match = content_match[1:] + if content_match.endswith('"'): + content_match = content_match[:-1] + + new_search = new_search[0: (match.start() - adjust_position):] + new_search[(match.end() - adjust_position) + 1::] + new_search = new_search[:(match.start() - adjust_position)] + '`' + mappings[content_match] + '` ' + new_search[(match.start() - adjust_position):] + adjust_position = match.end() - match.start() - len(mappings[content_match]) - 2 + + #generate macro configuration + + new_dict = {} + new_dict['definition'] = match.group() + new_dict['description'] = 'customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent.' + new_dict['name'] = mappings[content_match] + ordered_new_dict = OrderedDict(new_dict.items()) + new_file_name = mappings[content_match] + with open('macros/' + new_file_name + '.yml', 'w+' ) as outfile: + yaml.dump( new_dict , outfile , default_flow_style=False, sort_keys=False) + + return new_search + + +def change_filter_macro(object): + new_search = object['search'] + filter_macro = re.search("([a-z0-9_]*_filter)", new_search) + if filter_macro.group(1) != (object['name'].replace(' ', '_').replace('-', '_').replace('.', '_').replace('/', '_').lower() + '_filter'): + for match in re.finditer("([a-z0-9_]*_filter)", new_search): + new_search = new_search[0: match.start() - 1:] + new_search[match.end() + 1::] + new_search = new_search[:match.start() - 1] + '`' + object['name'].replace(' ', '_').replace('-', '_').replace('.', '_').replace('/', '_').lower() + '_filter' + '` ' + new_search[match.start():] + + return new_search + + +def change_response_task_variable(object): + if 'inputs' in object: + for input in object['inputs']: + if 'search' in object: + new_search = object['search'].replace("{" + input + "}", "$" + input + "$") + object['search'] = new_search + return object + + +if __name__ == "__main__": + generate_content() diff --git a/bin/doc-gen.py b/bin/doc-gen.py index 464491504c..8e1cdf0a12 100644 --- a/bin/doc-gen.py +++ b/bin/doc-gen.py @@ -3,6 +3,152 @@ import yaml import argparse from os import path import sys +import re +from jinja2 import Environment, FileSystemLoader + + +def load_objects(file_path): + files = [] + manifest_files = path.join(path.expanduser(REPO_PATH), file_path) + + for file in sorted(glob.glob(manifest_files)): + files.append(load_file(file)) + + return files + + +def load_file(file_path): + with open(file_path, 'r') as stream: + try: + file = list(yaml.safe_load_all(stream))[0] + except yaml.YAMLError as exc: + print(exc) + sys.exit("ERROR: reading {0}".format(file_path)) + return file + + +def prepare_content(stories, detections): + + # enrich stories with information from detections: data_models, mitre_ids, kill_chain_phases, nists + sto_to_data_models = {} + sto_to_mitre_attack_ids = {} + sto_to_kill_chain_phases = {} + sto_to_ciss = {} + sto_to_nists = {} + sto_to_det = {} + for detection in detections: + if 'analytics_story' in detection['tags']: + for story in detection['tags']['analytics_story']: + if story in sto_to_det.keys(): + sto_to_det[story].add(detection['name']) + else: + sto_to_det[story] = {detection['name']} + + data_model = parse_data_models_from_search(detection['search']) + if data_model: + if story in sto_to_data_models.keys(): + sto_to_data_models[story].add(data_model) + else: + sto_to_data_models[story] = {data_model} + + if 'mitre_attack_id' in detection['tags']: + if story in sto_to_mitre_attack_ids.keys(): + for mitre_attack_id in detection['tags']['mitre_attack_id']: + sto_to_mitre_attack_ids[story].add(mitre_attack_id) + else: + for mitre_attack_id in detection['tags']['mitre_attack_id']: + sto_to_mitre_attack_ids[story] = {mitre_attack_id} + + if 'kill_chain_phases' in detection['tags']: + if story in sto_to_kill_chain_phases.keys(): + for kill_chain in detection['tags']['kill_chain_phases']: + sto_to_kill_chain_phases[story].add(kill_chain) + else: + for kill_chain in detection['tags']['kill_chain_phases']: + sto_to_kill_chain_phases[story] = {kill_chain} + + if 'cis20' in detection['tags']: + if story in sto_to_ciss.keys(): + for cis in detection['tags']['cis20']: + sto_to_ciss[story].add(cis) + else: + for cis in detection['tags']['cis20']: + sto_to_ciss[story] = {cis} + + if 'nist' in detection['tags']: + if story in sto_to_nists.keys(): + for nist in detection['tags']['nist']: + sto_to_nists[story].add(nist) + else: + for nist in detection['tags']['nist']: + sto_to_nists[story] = {nist} + + for story in stories: + story['detections'] = sorted(sto_to_det[story['name']]) + if story['name'] in sto_to_data_models: + story['data_models'] = sorted(sto_to_data_models[story['name']]) + if story['name'] in sto_to_mitre_attack_ids: + story['mitre_attack_ids'] = sorted(sto_to_mitre_attack_ids[story['name']]) + if story['name'] in sto_to_kill_chain_phases: + story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) + if story['name'] in sto_to_ciss: + story['ciss'] = sorted(sto_to_ciss[story['name']]) + if story['name'] in sto_to_nists: + story['nists'] = sorted(sto_to_nists[story['name']]) + + #sort stories into categories + categories = [] + category_names = set() + for story in stories: + if 'category' in story['tags']: + category_names.add(story['tags']['category'][0]) + + for category_name in sorted(category_names): + new_category = {} + new_category['name'] = category_name + new_category['stories'] = [] + categories.append(new_category) + + for story in stories: + for category in categories: + if category['name'] == story['tags']['category'][0]: + category['stories'].append(story) + + return categories + + +def write_splunk_docs(stories, detections, OUTPUT_DIR): + + categories = prepare_content(stories, detections) + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('splunk_docs_categories.j2') + output_path = OUTPUT_DIR + "/splunk_docs_categories.wiki" + output = template.render(categories=categories) + with open(output_path, 'w') as f: + f.write(output) + + return len(stories), output_path + + +def write_markdown_docs(stories, detections, OUTPUT_DIR): + + categories = prepare_content(stories, detections) + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('stories_categories.j2') + output_path = OUTPUT_DIR + "/stories_categories.md" + output = template.render(categories=categories) + with open(output_path, 'w') as f: + f.write(output) + + return len(stories), output_path + + + + # function to get unique values @@ -314,7 +460,7 @@ def generate_stories(REPO_PATH, verbose): return complete_stories -def write_splunk_docs(stories, detections, OUTPUT_DIR): +def write_splunk_docs_bak(stories, detections, OUTPUT_DIR): paths = [] # Create conf files from analytics stories files @@ -402,7 +548,7 @@ def write_splunk_docs(stories, detections, OUTPUT_DIR): return story_count, paths -def write_markdown_docs(stories, detections, OUTPUT_DIR): +def write_markdown_docs_bak(stories, detections, OUTPUT_DIR): paths = [] # Create conf files from analytics stories files splunk_docs_output_path = OUTPUT_DIR + "/stories_categories.md" @@ -510,6 +656,12 @@ def write_markdown_docs(stories, detections, OUTPUT_DIR): return story_count, paths +def parse_data_models_from_search(search): + match = re.search('from\sdatamodel\s?=\s?([^\s.]*)',search) + if match is not None: + return match.group(1) + return False + if __name__ == "__main__": # grab arguments @@ -531,20 +683,21 @@ if __name__ == "__main__": gsd = args.gen_splunk_docs gmd = args.gen_markdown_docs - complete_stories = generate_stories(REPO_PATH, verbose) - complete_detections = generate_detections(REPO_PATH, complete_stories) + stories = load_objects("stories/*.yml") + detections = load_objects("detections/*.yml") + + # complete_stories = generate_stories(REPO_PATH, verbose) + # complete_detections = generate_detections(REPO_PATH, complete_stories) if gsd: - story_count, paths = write_splunk_docs(complete_stories, complete_detections, OUTPUT_DIR) - for p in paths: - print("{0} story documents have been successfully written to {1}".format(story_count, p)) + story_count, path = write_splunk_docs(stories, detections, OUTPUT_DIR) + print("{0} story documents have been successfully written to {1}".format(story_count, path)) else: print("--gen_splunk_docs was set to false, not generating splunk documentation") if gmd: - story_count, paths = write_markdown_docs(complete_stories, complete_detections, OUTPUT_DIR) - for p in paths: - print("{0} story documents have been successfully written to {1}".format(story_count, p)) + story_count, path = write_markdown_docs(stories, detections, OUTPUT_DIR) + print("{0} story documents have been successfully written to {1}".format(story_count, path)) else: print("--gen_splunk_docs was set to false, not generating splunk documentation") diff --git a/bin/generate.py b/bin/generate.py index ee2f245d1c..2afda820a8 100644 --- a/bin/generate.py +++ b/bin/generate.py @@ -11,6 +11,10 @@ from os import path import sys import datetime from jinja2 import Environment, FileSystemLoader +import re +from attackcti import attack_client +import csv + # global variables REPO_PATH = '' @@ -18,13 +22,13 @@ VERBOSE = False OUTPUT_PATH = '' -def load_objects(file_path): +def load_objects(file_path, VERBOSE): files = [] manifest_files = path.join(path.expanduser(REPO_PATH), file_path) - - for file in glob.glob(manifest_files): + for file in sorted(glob.glob(manifest_files)): + if VERBOSE: + print("processing manifest: {0}".format(file)) files.append(load_file(file)) - return files @@ -54,15 +58,55 @@ def generate_transforms_conf(lookups): return output_path -def generate_savedsearches_conf(detections, investigations, baselines): +def generate_savedsearches_conf(detections, response_tasks, baselines, deployments): + + for detection in detections: + # parse out data_models + data_model = parse_data_models_from_search(detection['search']) + if data_model: + detection['data_model'] = data_model + + matched_deployments = get_deployments(detection, deployments) + if len(matched_deployments): + detection['deployment'] = matched_deployments[-1] + nes_fields = get_nes_fields(detection['search'], detection['deployment']) + if len(nes_fields) > 0: + detection['nes_fields'] = nes_fields + + keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist'] + mappings = {} + for key in keys: + if key == 'mitre_attack': + if 'mitre_attack_id' in detection['tags']: + mappings[key] = detection['tags']['mitre_attack_id'] + else: + if key in detection['tags']: + mappings[key] = detection['tags'][key] + detection['mappings'] = mappings + + for baseline in baselines: + data_model = parse_data_models_from_search(baseline['search']) + if data_model: + baseline['data_model'] = data_model + + matched_deployments = get_deployments(baseline, deployments) + if len(matched_deployments): + baseline['deployment'] = matched_deployments[-1] + + for response_task in response_tasks: + if 'search' in response_task: + data_model = parse_data_models_from_search(response_task['search']) + if data_model: + response_task['data_model'] = data_model utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), trim_blocks=True) + j2_env.filters['custom_jinja2_enrichment_filter'] = custom_jinja2_enrichment_filter template = j2_env.get_template('savedsearches.j2') output_path = OUTPUT_PATH + "/default/savedsearches.conf" - output = template.render(detections=detections, investigations=investigations, baselines=baselines, time=utc_time) + output = template.render(detections=detections, baselines=baselines, response_tasks=response_tasks, time=utc_time) with open(output_path, 'w') as f: output = output.encode('ascii', 'ignore').decode('ascii') f.write(output) @@ -70,7 +114,23 @@ def generate_savedsearches_conf(detections, investigations, baselines): return output_path -def generate_analytics_story_conf(stories): +def generate_analytics_story_conf(stories, detections, response_tasks, baselines): + + sto_det = map_detection_to_stories(detections) + + sto_res = map_response_tasks_to_stories(response_tasks) + + sto_bas = map_baselines_to_stories(baselines) + + for story in stories: + if story['name'] in sto_det: + story['detections'] = list(sto_det[story['name']]) + if story['name'] in sto_res: + story['response_tasks'] = list(sto_res[story['name']]) + if story['name'] in sto_bas: + story['baselines'] = list(sto_bas[story['name']]) + + stories = prepare_stories(stories, detections) utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() @@ -85,7 +145,33 @@ def generate_analytics_story_conf(stories): return output_path -def generate_use_case_library_conf(stories, detections, investigations, baselines): +def generate_use_case_library_conf(stories, detections, response_tasks, baselines): + + sto_det = map_detection_to_stories(detections) + + sto_res = map_response_tasks_to_stories(response_tasks) + + for story in stories: + if story['name'] in sto_det: + story['detections'] = list(sto_det[story['name']]) + if story['name'] in sto_res: + story['response_tasks'] = list(sto_res[story['name']]) + story['searches'] = story['detections'] + story['response_tasks'] + else: + story['searches'] = story['detections'] + + for detection in detections: + + keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist'] + mappings = {} + for key in keys: + if key == 'mitre_attack': + if 'mitre_attack_id' in detection['tags']: + mappings[key] = detection['tags']['mitre_attack_id'] + else: + if key in detection['tags']: + mappings[key] = detection['tags'][key] + detection['mappings'] = mappings utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() @@ -94,7 +180,7 @@ def generate_use_case_library_conf(stories, detections, investigations, baseline template = j2_env.get_template('use_case_library.j2') output_path = OUTPUT_PATH + "/default/use_case_library.conf" output = template.render(stories=stories, detections=detections, - investigations=investigations, + response_tasks=response_tasks, baselines=baselines, time=utc_time) with open(output_path, 'w') as f: f.write(output) @@ -102,7 +188,17 @@ def generate_use_case_library_conf(stories, detections, investigations, baseline return output_path -def generate_macros_conf(macros): +def generate_macros_conf(macros, detections): + filter_macros = [] + for detection in detections: + new_dict = {} + new_dict['definition'] = 'search *' + new_dict['description'] = 'Update this macro to limit the output results to filter out false positives. ' + new_dict['name'] = detection['name']. \ + replace(' ', '_').replace('-', '_').replace('.', '_').replace('/', '_').lower() + '_filter' + filter_macros.append(new_dict) + + all_macros = macros + filter_macros utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() @@ -110,199 +206,263 @@ def generate_macros_conf(macros): trim_blocks=True) template = j2_env.get_template('macros.j2') output_path = OUTPUT_PATH + "/default/macros.conf" - output = template.render(macros=macros, time=utc_time) + output = template.render(macros=all_macros, time=utc_time) with open(output_path, 'w') as f: f.write(output) return output_path -def identify_next_steps(detections, investigations): - enriched_detections = [] - for detection in detections: - if 'splunk' in detection['detect']: - if 'correlation_rule' in detection['detect']['splunk']: - investigations_output = "" - has_phantom = False - next_steps = "" - if 'investigations' in detection: - for i in detection['investigations']: - if i['type'] == 'splunk': - investigations_output += "ESCU - {0}\\n".format(i['name']) - next_steps = "{\"version\": 1, \"data\": \"Recommended following steps:\\n\\n" - next_steps += "1.[[action|escu_investigate]]: Based on ESCU investigate \ - recommendations:\\ n%s\"}" % investigations_output - if i['type'] == 'phantom': - has_phantom = True +def generate_workbench_panels(response_tasks, stories): - # lets pull the playbook URL out from investigation object - playbook_url = '' - for inv in investigations: - if i['name'] == inv['name']: - playbook_url = inv['investigate']['phantom']['playbook_url'] - # construct next steps with the playbook info - playbook_next_steps_string = "Splunk>Phantom Response Playbook - Monitor enrichment of the \ - Splunk>Phantom Playbook called " + str(i['name']) + " and answer any \ - analyst prompt in Mission Control with a response decision. \ - Link to the playbook " + str(playbook_url) - next_steps = "{\"version\": 1, \"data\": \"Recommended following" - next_steps += ":\\n\\n1. [[action|runphantomplaybook]]: Phantom playbook " - next_steps += "recommendations:\\n%s\\n2. [[action|escu_investigate]]: " % (playbook_next_steps_string) - next_steps += "Based on ESCU investigate recommendations:\\n%s\"}" % (investigations_output) - if has_phantom: - detection['recommended_actions'] = 'runphantomplaybook, escu_investigate' - enriched_detections.append(detection) + sto_res = map_response_tasks_to_stories(response_tasks) - return enriched_detections - - -def map_investigations_to_detection(detections): - inv_det = {} - for detection in detections: - if 'investigations' in detection: - for investigation in detection['investigations']: - if not (investigation['id'] in inv_det): - inv_det[investigation['id']] = {detection['id']} - else: - inv_det[investigation['id']].add(detection['id']) - return inv_det - - -def map_baselines_to_detection(detections): - bas_det = {} - for detection in detections: - if 'baselines' in detection: - for baseline in detection['baselines']: - if not (baseline['id'] in bas_det): - bas_det[baseline['id']] = {detection['id']} - else: - bas_det[baseline['id']].add(detection['id']) - return bas_det - - -def map_detection_to_stories(stories): - det_sto = {} for story in stories: - for detection in story['detections']: - if not (detection['detection_id'] in det_sto): - det_sto[detection['detection_id']] = {story['name']} + if story['name'] in sto_res: + response_task_names = list(sto_res[story['name']]) + story['workbench_panels'] = [] + for response_task_name in response_task_names: + str = 'panel://workbench_panel_' + response_task_name[7:].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + story['workbench_panels'].append(str) + story['lowercase_name'] = story['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + + workbench_panel_objects = [] + for response_task in response_tasks: + if 'search' in response_task: + if 'inputs' in response_task: + response_file_name = response_task['name'].replace(' ', '_').replace('-','_').replace('.','_').replace('/','_').lower() + response_task['lowercase_name'] = response_file_name + workbench_panel_objects.append(response_task) + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('panel.j2') + output_path = OUTPUT_PATH + "/default/data/ui/panels/workbench_panel_" + response_file_name + ".xml" + output = template.render(search=response_task['search']) + with open(output_path, 'w') as f: + f.write(output) + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('es_investigations.j2') + output_path = OUTPUT_PATH + "/default/es_investigations.conf" + output = template.render(response_tasks=workbench_panel_objects, stories=stories) + with open(output_path, 'w') as f: + f.write(output) + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('workflow_actions.j2') + output_path = OUTPUT_PATH + "/default/workflow_actions.conf" + output = template.render(response_tasks=workbench_panel_objects) + with open(output_path, 'w') as f: + f.write(output) + + +def parse_data_models_from_search(search): + match = re.search(r'from\sdatamodel\s?=\s?([^\s.]*)', search) + if match is not None: + return match.group(1) + return False + + +def get_deployments(object, deployments): + matched_deployments = [] + + for deployment in deployments: + if 'analytics_story' in deployment['tags']: + if type(deployment['tags']['analytics_story']) is str: + tags_all_array = [deployment['tags']['analytics_story']] else: - det_sto[detection['detection_id']].add(story['name']) - return det_sto + tags_all_array = deployment['tags']['analytics_story'] + if tags_all_array[0] == 'all': + matched_deployments.append(deployment) + continue + + for tag in object['tags'].keys(): + if tag in deployment['tags'].keys(): + if type(object['tags'][tag]) is str: + tag_array = [object['tags'][tag]] + else: + tag_array = object['tags'][tag] + + for tag_value in tag_array: + if type(deployment['tags'][tag]) is str: + tag_array_deployment = [deployment['tags'][tag]] + else: + tag_array_deployment = deployment['tags'][tag] + + for tag_value_deployment in tag_array_deployment: + if tag_value == tag_value_deployment: + matched_deployments.append(deployment) + continue + + return matched_deployments -def enrich_investigations_with_stories(investigations, map_inv_det, map_det_sto): - enriched_investigations = [] - for investigation in investigations: - stories_set = set() - if investigation['id'] in map_inv_det: - for detection_id in map_inv_det[investigation['id']]: - if detection_id in map_det_sto: - stories_set = stories_set | map_det_sto[detection_id] +def get_nes_fields(search, deployment): + nes_fields_matches = [] + if 'notable' in deployment['alert_action']: + if 'nes_fields' in deployment['alert_action']['notable']: + for field in deployment['alert_action']['notable']['nes_fields']: + if (search.find(field + ' ') != -1): + nes_fields_matches.append(field) - investigation['stories'] = sorted(list(stories_set)) - enriched_investigations.append(investigation) - return enriched_investigations + return nes_fields_matches -def enrich_detections_with_stories(detections, map_det_sto): - enriched_detections = [] +def map_detection_to_stories(detections): + sto_det = {} for detection in detections: - stories_set = set() - if detection['id'] in map_det_sto: - stories_set = stories_set | map_det_sto[detection['id']] - detection['stories'] = sorted(list(stories_set)) - enriched_detections.append(detection) - return enriched_detections + if 'analytics_story' in detection['tags']: + for story in detection['tags']['analytics_story']: + if not (story in sto_det): + sto_det[story] = {str('ESCU - ' + detection['name'] + ' - Rule')} + else: + sto_det[story].add(str('ESCU - ' + detection['name'] + ' - Rule')) + return sto_det -def enrich_baselines_with_stories(baselines, map_bas_det, map_det_sto): - enriched_baselines = [] +def map_response_tasks_to_stories(response_tasks): + sto_res = {} + for response_task in response_tasks: + if 'tags' in response_task: + if 'analytics_story' in response_task['tags']: + for story in response_task['tags']['analytics_story']: + if not (story in sto_res): + sto_res[story] = {str('ESCU - ' + response_task['name'])} + else: + sto_res[story].add(str('ESCU - ' + response_task['name'])) + return sto_res + + +def map_baselines_to_stories(baselines): + sto_bas = {} for baseline in baselines: - stories_set = set() - if baseline['id'] in map_bas_det: - for baseline_id in map_bas_det[baseline['id']]: - if baseline_id in map_det_sto: - stories_set = stories_set | map_det_sto[baseline_id] + if 'tags' in baseline: + if 'analytics_story' in baseline['tags']: + for story in baseline['tags']['analytics_story']: + if not (story in sto_bas): + sto_bas[story] = {str('ESCU - ' + baseline['name'])} + else: + sto_bas[story].add(str('ESCU - ' + baseline['name'])) + return sto_bas - baseline['stories'] = sorted(list(stories_set)) - enriched_baselines.append(baseline) - return enriched_baselines +def custom_jinja2_enrichment_filter(string, object): + customized_string = string + for key in object.keys(): + customized_string = customized_string.replace("%" + key + "%", str(object[key])) + + for key in object['tags'].keys(): + customized_string = customized_string.replace("%" + key + "%", str(object['tags'][key])) + + return customized_string -def enrich_stories(stories, detections, investigations, baselines): - enriched_stories = [] +def prepare_stories(stories, detections): + + # enrich stories with information from detections: data_models, mitre_ids, kill_chain_phases, nists + sto_to_data_models = {} + sto_to_mitre_attack_ids = {} + sto_to_kill_chain_phases = {} + sto_to_ciss = {} + sto_to_nists = {} + sto_to_det = {} + for detection in detections: + if 'analytics_story' in detection['tags']: + for story in detection['tags']['analytics_story']: + if story in sto_to_det.keys(): + sto_to_det[story].add(str('ESCU - ' + detection['name'] + ' - Rule')) + else: + sto_to_det[story] = {str('ESCU - ' + detection['name'] + ' - Rule')} + + data_model = parse_data_models_from_search(detection['search']) + if data_model: + if story in sto_to_data_models.keys(): + sto_to_data_models[story].add(data_model) + else: + sto_to_data_models[story] = {data_model} + + if 'mitre_attack_id' in detection['tags']: + if story in sto_to_mitre_attack_ids.keys(): + for mitre_attack_id in detection['tags']['mitre_attack_id']: + sto_to_mitre_attack_ids[story].add(mitre_attack_id) + else: + for mitre_attack_id in detection['tags']['mitre_attack_id']: + sto_to_mitre_attack_ids[story] = {mitre_attack_id} + + if 'kill_chain_phases' in detection['tags']: + if story in sto_to_kill_chain_phases.keys(): + for kill_chain in detection['tags']['kill_chain_phases']: + sto_to_kill_chain_phases[story].add(kill_chain) + else: + for kill_chain in detection['tags']['kill_chain_phases']: + sto_to_kill_chain_phases[story] = {kill_chain} + + if 'cis20' in detection['tags']: + if story in sto_to_ciss.keys(): + for cis in detection['tags']['cis20']: + sto_to_ciss[story].add(cis) + else: + for cis in detection['tags']['cis20']: + sto_to_ciss[story] = {cis} + + if 'nist' in detection['tags']: + if story in sto_to_nists.keys(): + for nist in detection['tags']['nist']: + sto_to_nists[story].add(nist) + else: + for nist in detection['tags']['nist']: + sto_to_nists[story] = {nist} + for story in stories: - providing_technologies = set() - data_models = set() - detection_names = [] - mappings = dict() - mappings["cis20"] = set() - mappings["kill_chain_phases"] = set() - mappings["mitre_attack"] = set() - mappings["nist"] = set() - mappings["mitre_technique_id"] = set() - searches = [] + story['detections'] = sorted(sto_to_det[story['name']]) + if story['name'] in sto_to_data_models: + story['data_models'] = sorted(sto_to_data_models[story['name']]) + if story['name'] in sto_to_mitre_attack_ids: + story['mitre_attack'] = sorted(sto_to_mitre_attack_ids[story['name']]) + if story['name'] in sto_to_kill_chain_phases: + story['kill_chain_phases'] = sorted(sto_to_kill_chain_phases[story['name']]) + if story['name'] in sto_to_ciss: + story['cis20'] = sorted(sto_to_ciss[story['name']]) + if story['name'] in sto_to_nists: + story['nist'] = sorted(sto_to_nists[story['name']]) - for detection in story['detections']: - for detection_obj in detections: - if detection['detection_id'] == detection_obj['id']: - if 'providing_technologies' in detection_obj['data_metadata']: - providing_technologies = providing_technologies | set(detection_obj - ['data_metadata']['providing_technologies']) - if 'data_models' in detection_obj['data_metadata']: - data_models = data_models | set(detection_obj['data_metadata']['data_models']) - if detection_obj['type'] == 'splunk': - detection_names.append("ESCU - " + detection_obj['name'] + " - Rule") - - for key in detection_obj['mappings']: - mappings[key] = mappings[key] | set(detection_obj['mappings'][key]) - - for key in mappings.keys(): - mappings[key] = sorted(list(mappings[key])) + keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist'] + mappings = {} + for key in keys: + if key in story: + mappings[key] = story[key] story['mappings'] = mappings - story['detection_names'] = sorted(detection_names) - searches = sorted(detection_names) - investigation_names = [] + return stories - for investigation in investigations: - for s in investigation['stories']: - if s == story['name']: - if 'providing_technologies' in investigation['data_metadata']: - providing_technologies = providing_technologies | set(investigation - ['data_metadata']['providing_technologies']) - if 'data_models' in investigation['data_metadata']: - data_models = data_models | set(investigation['data_metadata']['data_models']) - if investigation['type'] == 'splunk': - investigation_names.append("ESCU - " + investigation['name']) - story['investigation_names'] = sorted(investigation_names) - searches = searches + sorted(investigation_names) +def generate_mitre_lookup(): - baseline_names = [] + csv_mitre_rows = [["mitre_id", "technique", "tactics", "groups"]] - for baseline in baselines: - for s in baseline['stories']: - if s == story['name']: - if 'providing_technologies' in baseline['data_metadata']: - providing_technologies = providing_technologies | set(baseline['data_metadata']['providing_technologies']) - if 'data_models' in baseline['data_metadata']: - data_models = data_models | set(baseline['data_metadata']['data_models']) - if baseline['type'] == 'splunk': - baseline_names.append("ESCU - " + baseline['name']) + lift = attack_client() + all_enterprise = lift.get_enterprise(stix_format=False) + enterprise_relationships = lift.get_enterprise_relationships() + enterprise_groups = lift.get_enterprise_groups() - story['baseline_names'] = sorted(baseline_names) - searches = searches + sorted(baseline_names) + for technique in all_enterprise['techniques']: + apt_groups = [] + for relationship in enterprise_relationships: + if (relationship['target_ref'] == technique['id']) and relationship['source_ref'].startswith('intrusion-set'): + for group in enterprise_groups: + if relationship['source_ref'] == group['id']: + apt_groups.append(group['name']) - story['providing_technologies'] = sorted(list(providing_technologies)) - story['data_models'] = sorted(list(data_models)) - story['searches'] = searches + if len(apt_groups) == 0: + apt_groups.append('no') + csv_mitre_rows.append([technique['technique_id'], technique['technique'], '|'.join(technique['tactic']).replace('-',' ').title(), '|'.join(apt_groups)]) - enriched_stories.append(story) + with open('lookups/mitre_enrichment.csv', 'w', newline='') as file: + writer = csv.writer(file) + writer.writerows(csv_mitre_rows) - return enriched_stories if __name__ == "__main__": @@ -319,44 +479,44 @@ if __name__ == "__main__": REPO_PATH = args.path OUTPUT_PATH = args.output VERBOSE = args.verbose + stories = load_objects("stories/*.yml", VERBOSE) + macros = load_objects("macros/*.yml", VERBOSE) + lookups = load_objects("lookups/*.yml", VERBOSE) + baselines = load_objects("baselines/*.yml", VERBOSE) + detections = load_objects("detections/*.yml", VERBOSE) + responses = load_objects("responses/*.yml", VERBOSE) + response_tasks = load_objects("response_tasks/*.yml", VERBOSE) + deployments = load_objects("deployments/*.yml", VERBOSE) - stories = load_objects("stories/*.yml") - macros = load_objects("macros/*.yml") - lookups = load_objects("lookups/*.yml") - detections = load_objects("detections/*.yml") - investigations = load_objects("investigations/*.yml") - baselines = load_objects("baselines/*.yml") - - detections = identify_next_steps(detections, investigations) - - map_inv_det = map_investigations_to_detection(detections) - map_det_sto = map_detection_to_stories(stories) - map_bas_det = map_baselines_to_detection(detections) - detections = enrich_detections_with_stories(detections, map_det_sto) - investigations = enrich_investigations_with_stories(investigations, map_inv_det, map_det_sto) - baselines = enrich_baselines_with_stories(baselines, map_bas_det, map_det_sto) - stories = enrich_stories(stories, detections, investigations, baselines) + try: + if VERBOSE: + print("generating Mitre lookups") + generate_mitre_lookup() + except: + print("WARNING: Generation of Mitre lookup failed.") lookups_path = generate_transforms_conf(lookups) detections = sorted(detections, key=lambda d: d['name']) - investigations = sorted(investigations, key=lambda i: i['name']) + response_tasks = sorted(response_tasks, key=lambda i: i['name']) baselines = sorted(baselines, key=lambda b: b['name']) - detection_path = generate_savedsearches_conf(detections, investigations, baselines) + detection_path = generate_savedsearches_conf(detections, response_tasks, baselines, deployments) stories = sorted(stories, key=lambda s: s['name']) - story_path = generate_analytics_story_conf(stories) + story_path = generate_analytics_story_conf(stories, detections, response_tasks, baselines) - use_case_lib_path = generate_use_case_library_conf(stories, detections, investigations, baselines) + use_case_lib_path = generate_use_case_library_conf(stories, detections, response_tasks, baselines) macros = sorted(macros, key=lambda m: m['name']) - macros_path = generate_macros_conf(macros) + macros_path = generate_macros_conf(macros, detections) + + generate_workbench_panels(response_tasks, stories) + if VERBOSE: print("{0} stories have been successfully written to {1}".format(len(stories), story_path)) - print("{0} stories have been successfully written to {1}".format(len(stories), use_case_lib_path)) print("{0} detections have been successfully written to {1}".format(len(detections), detection_path)) - print("{0} investigations have been successfully written to {1}".format(len(investigations), detection_path)) + print("{0} response tasks have been successfully written to {1}".format(len(response_tasks), detection_path)) print("{0} baselines have been successfully written to {1}".format(len(baselines), detection_path)) print("{0} macros have been successfully written to {1}".format(len(macros), macros_path)) print("security content generation completed..") diff --git a/bin/jinja2_templates/analytic_stories.j2 b/bin/jinja2_templates/analytic_stories.j2 index c8053c48af..fd88d98915 100644 --- a/bin/jinja2_templates/analytic_stories.j2 +++ b/bin/jinja2_templates/analytic_stories.j2 @@ -9,18 +9,32 @@ {% for story in stories %} [{{ story.name }}] -category = {{ story.category[0] }} -creation_date = {{ story.creation_date }} -modification_date = {{ story.modification_date }} +category = {{ story.tags.category[0] }} +creation_date = {{ story.date }} +modification_date = {{ story.date }} id = {{ story.id }} version = {{ story.version }} reference = {{ story.references | tojson }} -detection_searches = {{ story.detection_names | tojson }} +detection_searches = {{ story.detections | tojson }} +{% if story.mappings is defined %} mappings = {{ story.mappings | tojson }} -investigative_searches = {{ story.investigation_names | tojson }} -support_searches = {{ story.baseline_names | tojson }} +{% endif %} +{% if story.response_tasks is defined %} +investigative_searches = {{ story.response_tasks | tojson }} +{% else %} +investigative_searches = [] +{% endif %} +{% if story.baselines is defined %} +support_searches = {{ story.baselines | tojson }} +{% else %} +support_searches = [] +{% endif %} +{% if story.data_models is defined %} data_models = {{ story.data_models | tojson }} -providing_technologies = {{ story.providing_technologies | tojson }} +{% else %} +data_models = [] +{% endif %} +providing_technologies = none description = {{ story.description }} {% if story.narrative is defined %} narrative = {{ story.narrative }} diff --git a/bin/jinja2_templates/es_investigations.j2 b/bin/jinja2_templates/es_investigations.j2 new file mode 100644 index 0000000000..865efd48ec --- /dev/null +++ b/bin/jinja2_templates/es_investigations.j2 @@ -0,0 +1,40 @@ + +{% for story in stories %} +[panel_group://workbench_panel_group_{{ story.lowercase_name}}] +label = {{ story.name }} +description = {{ story.description }} +disabled = 0 +panels = {{ story.workbench_panels | tojson }} + +{% endfor %} + + +{% for response_task in response_tasks %} +[panel://workbench_panel_{{ response_task.lowercase_name }}] +label = {{ response_task.name }} +description = {{ response_task.description }} +disabled = 0 +tokens = {\ +{% for token in response_task.inputs %} +{% if token == 'user' %} + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }{% else %} + "{{ token }}": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }{% endif %}{{ "," if not loop.last }}\ +{% endfor %} +}\ + + +{% endfor %} diff --git a/bin/jinja2_templates/panel.j2 b/bin/jinja2_templates/panel.j2 new file mode 100644 index 0000000000..45be63462f --- /dev/null +++ b/bin/jinja2_templates/panel.j2 @@ -0,0 +1,9 @@ + + + + {{ search }} + + + +
+ diff --git a/bin/jinja2_templates/savedsearches.j2 b/bin/jinja2_templates/savedsearches.j2 index 8e50fabe6a..68f28ae60f 100644 --- a/bin/jinja2_templates/savedsearches.j2 +++ b/bin/jinja2_templates/savedsearches.j2 @@ -7,21 +7,18 @@ ### ESCU DETECTIONS ### - {% for detection in detections %} [ESCU - {{ detection.name }} - Rule] action.escu = 0 action.escu.enabled = 1 description = {{ detection.description }} action.escu.mappings = {{ detection.mappings | tojson }} -{% if detection.data_metadata.data_models is defined %} -action.escu.data_models = {{ detection.data_metadata.data_models | tojson }} -{% endif %} -{% if detection.eli5 is defined %} -action.escu.eli5 = {{ detection.eli5 }} +{% if detection.data_model is defined %} +action.escu.data_models = [{{ detection.data_model | tojson }}] {% else %} -action.escu.eli5 = none +action.escu.data_models = [] {% endif %} +action.escu.eli5 = {{ detection.description }} {% if detection.how_to_implement is defined %} action.escu.how_to_implement = {{ detection.how_to_implement }} {% else %} @@ -32,67 +29,53 @@ action.escu.known_false_positives = {{ detection.known_false_positives }} {% else %} action.escu.known_false_positives = None {% endif %} -action.escu.creation_date = {{ detection.creation_date }} -action.escu.modification_date = {{ detection.modification_date }} -action.escu.confidence = {{ detection.confidence }} +action.escu.creation_date = {{ detection.date }} +action.escu.modification_date = {{ detection.date }} +action.escu.confidence = high action.escu.full_search_name = ESCU - {{ detection.name }} - Rule action.escu.search_type = detection -{% if detection.asset_type is defined %} -action.escu.asset_at_risk = {{ detection.asset_type }} +{% if detection.providing_technologies is defined %} +action.escu.providing_technologies = {{ detection.providing_technologies | tojson }} +{% else %} +action.escu.providing_technologies = [] {% endif %} -{% if detection.entities is defined %} -action.escu.fields_required = {{ detection.entities | tojson }} -action.escu.entities = {{ detection.entities | tojson }} +{% if detection.tags.analytics_story is defined %} +action.escu.analytic_story = {{ detection.tags.analytics_story | tojson }} +{% else %} +action.escu.analytic_story = [] {% endif %} -{% if detection.data_metadata.providing_technologies is defined %} -action.escu.providing_technologies = {{ detection.data_metadata.providing_technologies | tojson }} -{% endif %} -action.escu.analytic_story = {{ detection.stories | tojson | replace("\\u0026","&") }} -{% if detection.detect.splunk is defined %} -{% set correlation_rule = detection.detect.splunk.correlation_rule %} -{% elif detection.detect.uba is defined %} -{% set correlation_rule = detection.detect.uba.correlation_rule %} -{% endif %} -{% if correlation_rule.schedule.cron_schedule is defined %} -cron_schedule = {{ correlation_rule.schedule.cron_schedule }} -{% endif %} -{% if correlation_rule.schedule.earliest_time is defined %} -dispatch.earliest_time = {{ correlation_rule.schedule.earliest_time }} -{% endif %} -{% if correlation_rule.schedule.latest_time is defined %} -dispatch.latest_time = {{ correlation_rule.schedule.latest_time }} -{% endif %} -{% if correlation_rule is defined %} +cron_schedule = {{ detection.deployment.scheduling.cron_schedule }} +dispatch.earliest_time = {{ detection.deployment.scheduling.earliest_time }} +dispatch.latest_time = {{ detection.deployment.scheduling.latest_time }} action.correlationsearch.enabled = 1 -action.correlationsearch.label = {{ detection.name }} -{% if correlation_rule.notable is defined %} +action.correlationsearch.label = ESCU - {{ detection.name }} - Rule +{% if detection.deployment.scheduling.schedule_window is defined %} +schedule_window = {{ detection.deployment.scheduling.schedule_window }} +{% endif %} +{% if detection.deployment.alert_action.notable is defined %} action.notable = 1 +{% if detection.nes_fields is defined %} +action.notable.param.nes_fields = {{ detection.nes_fields }} {% endif %} -{% if correlation_rule.notable.nes_fields is defined %} -action.notable.param.nes_fields = {{ correlation_rule.notable.nes_fields }} -action.notable.param.rule_description = {{ correlation_rule.notable.rule_description }} -action.notable.param.rule_title = {{ correlation_rule.notable.rule_title }} -action.notable.param.security_domain = {{ detection.security_domain }} -action.notable.param.severity = {{ detection.confidence }} +action.notable.param.rule_description = {{ detection.deployment.alert_action.notable.rule_description | custom_jinja2_enrichment_filter(detection) }} +action.notable.param.rule_title = {{ detection.deployment.alert_action.notable.rule_title | custom_jinja2_enrichment_filter(detection) }} +action.notable.param.security_domain = {{ detection.tags.security_domain }} +action.notable.param.severity = high {% endif %} -{% if (correlation_rule.notable.drilldown_name is defined) and (correlation_rule.notable.drilldown_search is defined) %} -action.notable.param.drilldown_name = {{ correlation_rule.notable.drilldown_name }} -action.notable.param.drilldown_search = {{ correlation_rule.notable.drilldown_search }} +{% if detection.deployment.alert_action.email is defined %} +action.email.subject.alert = {{ detection.deployment.alert_action.email.subject | custom_jinja2_enrichment_filter(detection) }} +action.email.to = {{ detection.deployment.alert_action.email.to }} +action.email.message.alert = {{ detection.deployment.alert_action.email.message | custom_jinja2_enrichment_filter(detection) }} +action.email.useNSSubject = 1 {% endif %} -{% if correlation_rule.risk is defined %} +{% if detection.deployment.alert_action.risk is defined %} action.risk = 1 -action.risk.param._risk_object = {{ correlation_rule.risk.risk_object }} -action.risk.param._risk_object_type = {{ correlation_rule.risk.risk_object_type[0] }} -action.risk.param._risk_score = {{ correlation_rule.risk.risk_score }} +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 {% endif %} -{% if correlation_rule.suppress is defined %} alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = {{ correlation_rule.suppress.suppress_fields }} -alert.suppress.period = {{ correlation_rule.suppress.suppress_period }} -{% endif %} -{% endif %} action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -101,118 +84,96 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -{% if detection.detect.splunk is defined %} -search = {{ detection.detect.splunk.correlation_rule.search }} -{% elif detection.detect.uba is defined %} -search = {{ detection.detect.uba.correlation_rule.search }} -{% endif %} +search = {{ detection.search }} {% endfor %} ### END ESCU DETECTIONS ### -### ESCU INVESTIGATIONS ### - -{% for investigation in investigations %} -{% if investigation.type != 'phantom' %} -[ESCU - {{ investigation.name }}] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = investigative -action.escu.full_search_name = ESCU - {{ investigation.name }} -description = {{ investigation.description }} -action.escu.creation_date = {{ investigation.creation_date }} -action.escu.modification_date = {{ investigation.modification_date }} -action.escu.analytic_story = {{ investigation.stories | tojson | replace("\\u0026","&") }} -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 86400 -{% if investigation.data_metadata.data_models is defined %} -action.escu.data_models = {{ investigation.data_metadata.data_models | tojson }} -{% endif %} -{% if investigation.data_metadata.providing_technologies is defined %} -action.escu.providing_technologies = {{ investigation.data_metadata.providing_technologies | tojson }} -{% endif %} -{% if investigation.eli5 is defined %} -action.escu.eli5 = {{ investigation.eli5 }} -{% else %} -action.escu.eli5 = none -{% endif %} -{% if investigation.how_to_implement is defined %} -action.escu.how_to_implement = {{ investigation.how_to_implement }} -{% else %} -action.escu.how_to_implement = none -{% endif %} -{% if investigation.known_false_positives is defined %} -action.escu.known_false_positives = {{ investigation.known_false_positives }} -{% else %} -action.escu.known_false_positives = None at this time -{% endif %} -{% if investigation.entities is defined %} -action.escu.fields_required = {{ investigation.entities | tojson }} -action.escu.entities = {{ investigation.entities | tojson }} -{% endif %} -disabled = true -schedule_window = auto -is_visible = false -search = {{ investigation.investigate.splunk.search }} - -{% endif %} -{% endfor %} - -### END ESCU INVESTIGATIONS ### - ### ESCU BASELINES ### {% for baseline in baselines %} -[ESCU - {{ baseline.name }}] +[ESCU - {{ baseline.name }} - Baseline] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support action.escu.full_search_name = ESCU - {{ baseline.name }} description = {{ baseline.description }} -action.escu.creation_date = {{ baseline.creation_date }} -action.escu.modification_date = {{ baseline.modification_date }} -action.escu.analytic_story = {{ baseline.stories | tojson | replace("\\u0026","&") }} -{% if baseline.data_metadata.data_models is defined %} -action.escu.data_models = {{ baseline.data_metadata.data_models | tojson }} -{% endif %} -{% if baseline.baseline.splunk.schedule.earliest_time is defined %} -dispatch.earliest_time = {{ baseline.baseline.splunk.schedule.earliest_time }} -{% endif %} -{% if baseline.baseline.splunk.schedule.latest_time is defined %} -dispatch.latest_time = {{ baseline.baseline.splunk.schedule.latest_time }} -{% endif %} -{% if baseline.data_metadata.providing_technologies is defined %} -action.escu.providing_technologies = {{ baseline.data_metadata.providing_technologies | tojson }} -{% endif %} -{% if baseline.eli5 is defined %} -action.escu.eli5 = {{ baseline.eli5 }} +action.escu.creation_date = {{ baseline.date }} +action.escu.modification_date = {{ baseline.date }} +{% if baseline.tags.analytics_story is defined %} +action.escu.analytic_story = {{ baseline.tags.analytics_story | tojson }} {% else %} -action.escu.eli5 = none +action.escu.analytic_story = [] {% endif %} +{% if baseline.data_model is defined %} +action.escu.data_models = [{{ baseline.data_model | tojson }}] +{% else %} +action.escu.data_models = [] +{% endif %} +dispatch.earliest_time = {{ baseline.deployment.scheduling.earliest_time }} +dispatch.latest_time = {{ baseline.deployment.scheduling.latest_time }} +{% if baseline.deployment.scheduling.schedule_window is defined %} +schedule_window = {{ baseline.deployment.scheduling.schedule_window }} +{% endif %} +{% if baseline.providing_technologies is defined %} +action.escu.providing_technologies = {{ baseline.providing_technologies | tojson }} +{% else %} +action.escu.providing_technologies = [] +{% endif %} +action.escu.eli5 = {{ baseline.description }} {% if baseline.how_to_implement is defined %} action.escu.how_to_implement = {{ baseline.how_to_implement }} {% else %} action.escu.how_to_implement = none {% endif %} -{% if baseline.known_false_positives is defined %} -action.escu.known_false_positives = {{ baseline.known_false_positives }} +disabled = true +is_visible = false +search = {{ baseline.search }} + +{% endfor %} + + +### ESCU RESPONSE TASKS ### + +{% for response_task in response_tasks %} +{% if response_task.search is defined %} +[ESCU - {{ response_task.name }} - Response Task] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = investigative +action.escu.full_search_name = ESCU - {{ response_task.name }} +description = {{ response_task.description }} +action.escu.creation_date = {{ response_task.date }} +action.escu.modification_date = {{ response_task.date }} +{% if response_task.tags is defined %} +action.escu.analytic_story = {{ response_task.tags.analytics_story | tojson }} {% else %} +action.escu.analytic_story = [] +{% endif %} +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] +{% if response_task.data_model is defined %} +action.escu.data_models = [{{ response_task.data_model | tojson}}] +{% else %} +action.escu.data_models = [] +{% endif %} +action.escu.eli5 = {{ response_task.description }} +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -{% endif %} -{% if baseline.entities is defined %} -action.escu.fields_required = {{ baseline.entities | tojson }} -action.escu.entities = {{ baseline.entities | tojson }} -{% endif %} disabled = true schedule_window = auto is_visible = false -search = {{ baseline.baseline.splunk.search }} +search = {{ response_task.search }} +{% endif %} {% endfor %} + +### END ESCU RESPONSE TASKS ### + ### USAGE DASHBOARD CONFIGURATIONS ### [escu-metrics-usage] diff --git a/bin/jinja2_templates/splunk_docs_categories.j2 b/bin/jinja2_templates/splunk_docs_categories.j2 new file mode 100644 index 0000000000..b0582430a8 --- /dev/null +++ b/bin/jinja2_templates/splunk_docs_categories.j2 @@ -0,0 +1,65 @@ += Use Case Categories= +The collapse... + + +{% for category in categories %} +=={{ category.name }}== + +{% for story in category.stories %} +==={{ story.name }}=== + +{{ story.description }} + +
+
+ +====Narrative==== +{{ story.narrative }} + +====Detections==== +{% for detection in story.detections %} +* {{ detection }} +{% endfor %} + +====Data Models==== +{% for data_model in story.data_models %} +* {{ data_model }} +{% endfor %} + +====Tags==== + +=====ATT&CK===== +{% for mitre_attack_id in story.mitre_attack_ids %} +* {{ mitre_attack_id }} +{% endfor %} + +=====Kill Chain Phases===== +{% for kill_chain_phase in story.kill_chain_phases %} +* {{ kill_chain_phase }} +{% endfor %} + +=====CIS===== +{% for cis in story.ciss %} +* {{ cis }} +{% endfor %} + +=====NIST===== +{% for nist in story.nists %} +* {{ nist }} +{% endfor %} + +====References==== +{% for reference in story.references %} +* {{ reference }} +{% endfor %} + +date = {{ story.date }} + +version = {{ story.version }} + +
+
+ +{% endfor %} + +{% endfor %} diff --git a/bin/jinja2_templates/stories_categories.j2 b/bin/jinja2_templates/stories_categories.j2 new file mode 100644 index 0000000000..7862cd9ed5 --- /dev/null +++ b/bin/jinja2_templates/stories_categories.j2 @@ -0,0 +1,68 @@ + +# Categories +Analytics stories organized by categories +{% for category in categories %} + +* [{{ category.name }}](#{{ category.name | replace(' ','-') }}) +{% endfor %} + + +{% for category in categories %} +## {{ category.name }} +{% for story in category.stories %} + +* [{{ story.name }}](#{{ story.name | replace(' ','-') }}) +{% endfor %} + +{% for story in category.stories %} +### {{ story.name }} +* id = {{ story.id }} +* date = {{ story.date }} +* version = {{ story.version }} + +#### Description +{{ story.description }} + +#### Narrative +{{ story.narrative }} + +#### Detections +{% for detection in story.detections %} +* {{ detection }} +{% endfor %} + +#### Data Models +{% for data_model in story.data_models %} +* {{ data_model }} +{% endfor %} + +#### Mappings + +##### ATT&CK +{% for mitre_attack_id in story.mitre_attack_ids %} +* {{ mitre_attack_id }} +{% endfor %} + +##### Kill Chain Phases +{% for kill_chain_phase in story.kill_chain_phases %} +* {{ kill_chain_phase }} +{% endfor %} + +###### CIS +{% for cis in story.ciss %} +* {{ cis }} +{% endfor %} + +##### NIST +{% for nist in story.nists %} +* {{ nist }} +{% endfor %} + +##### References +{% for reference in story.references %} +* {{ reference }} +{% endfor %} + +{% endfor %} + +{% endfor %} diff --git a/bin/jinja2_templates/use_case_library.j2 b/bin/jinja2_templates/use_case_library.j2 index b1e6d06abb..292ca19b92 100644 --- a/bin/jinja2_templates/use_case_library.j2 +++ b/bin/jinja2_templates/use_case_library.j2 @@ -9,12 +9,12 @@ {% for story in stories %} [analytic_story://{{ story.name }}] -category = {{ story.category[0] }} -last_updated = {{ story.modification_date }} +category = {{ story.tags.category[0] }} +last_updated = {{ story.date }} version = {{ story.version }} references = {{ story.references | tojson }} -maintainers = {{ story.maintainers | tojson }} -spec_version = {{ story.spec_version }} +maintainers = {{ story.author | tojson }} +spec_version = 3 searches = {{ story.searches | tojson }} description = {{ story.description }} {% if story.narrative is defined %} @@ -29,48 +29,55 @@ narrative = {{ story.narrative }} {% for detection in detections %} [savedsearch://ESCU - {{ detection.name }} - Rule] type = detection -asset_type = {{ detection.asset_type }} -confidence = {{ detection.confidence }} -explanation = {{ detection.eli5 }} +asset_type = {{ detection.tags.asset_type }} +confidence = medium +explanation = {{ detection.description }} +{% if detection.how_to_implement is defined %} how_to_implement = {{ detection.how_to_implement }} +{% else %} +how_to_implement = none +{% endif %} annotations = {{ detection.mappings | tojson }} known_false_positives = {{ detection.known_false_positives }} -providing_technologies = {{ detection.data_metadata.providing_technologies | tojson }} +providing_technologies = [] {% endfor %} ### END DETECTIONS ### -### INVESTIGATIONS ### +### RESPONSE TASKS ### -{% for investigation in investigations %} -[savedsearch://ESCU - {{ investigation.name }}] +{% for response_task in response_tasks %} +[savedsearch://ESCU - {{ response_task.name }}] type = investigation explanation = none -how_to_implement = {{ investigation.how_to_implement }} -{% if investigation.known_false_positives is defined %} -known_false_positives = {{ investigation.known_false_positives }} +{% if response_task.how_to_implement is defined %} +how_to_implement = {{ response_task.how_to_implement }} {% else %} -known_false_positives = None at this time -{% endif %} -{% if investigation.investigate.splunk is defined %} -earliest_time_offset = {{ investigation.investigate.splunk.schedule.earliest_time }} -latest_time_offset = {{ investigation.investigate.splunk.schedule.latest_time }} -{% else %} -earliest_time_offset = {{ investigation.investigate.phantom.schedule.earliest_time }} -latest_time_offset = {{ investigation.investigate.phantom.schedule.latest_time }} +how_to_implement = none {% endif %} +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 {% endfor %} -### END INVESTIGATIONS ### +### END RESPONSE TASKS ### ### BASELINES ### {% for baseline in baselines %} [savedsearch://ESCU - {{ baseline.name }}] type = support -explanation = {{ baseline.eli5 }} +explanation = {{ baseline.description }} +{% if baseline.how_to_implement is defined %} how_to_implement = {{ baseline.how_to_implement }} +{% else %} +how_to_implement = none +{% endif %} +{% if baseline.known_false_positives is defined %} known_false_positives = {{ baseline.known_false_positives }} -providing_technologies = {{ baseline.data_metadata.providing_technologies | tojson }} +{% else %} +known_false_positives = not defined +{% endif %} +providing_technologies = none {% endfor %} ### END ESCU BASELINES ### diff --git a/bin/jinja2_templates/workflow_actions.j2 b/bin/jinja2_templates/workflow_actions.j2 new file mode 100644 index 0000000000..82b25cfe54 --- /dev/null +++ b/bin/jinja2_templates/workflow_actions.j2 @@ -0,0 +1,18 @@ + +{% for response_task in response_tasks %} +{% if response_task.inputs|length == 1 %} +[workbench_panel_{{ response_task.lowercase_name }}]] +label = Workbench - {{ response_task.name }} +type = link +fields = *{{ response_task.inputs[0] }}* +display_location = field_menu +{% if response_task.inputs[0] == "user" %} +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_{{ response_task.lowercase_name }}&drilldown_field=$@field_name$&use_drilldown_time=true +{% else %} +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_{{ response_task.lowercase_name }}&drilldown_field=$@field_name$&use_drilldown_time=true +{% endif %} +link.target = blank +link.method = get +{% endif %} + +{% endfor %} diff --git a/bin/validate.py b/bin/validate.py index 194e4d860f..44540b146e 100644 --- a/bin/validate.py +++ b/bin/validate.py @@ -10,28 +10,25 @@ import jsonschema import yaml import sys import argparse +import datetime +import string +import re from os import path -def validate_object(REPO_PATH, schema_path, manifest_path, return_objects, verbose, lookups=None, macros=None): - ''' Validate scheme ''' +def validate_schema(REPO_PATH, type, objects): + error = False + errors = [] - # uuids - baselines_uuids = [] - story_uuids = [] - detection_uuids = [] - investigation_uuids = [] - - schema_file = path.join(path.expanduser(REPO_PATH), schema_path) + schema_file = path.join(path.expanduser(REPO_PATH), 'spec/' + type + '.spec.json') try: schema = json.loads(open(schema_file, 'rb').read()) except IOError: - print("ERROR: reading baseline schema file {0}".format(schema_file)) + print("ERROR: reading schema file {0}".format(schema_file)) - objects = {} - manifest_files = path.join(path.expanduser(REPO_PATH), manifest_path) + manifest_files = path.join(path.expanduser(REPO_PATH), type + '/*.yml') for manifest_file in glob.glob(manifest_files): if verbose: @@ -49,365 +46,146 @@ def validate_object(REPO_PATH, schema_path, manifest_path, return_objects, verbo try: jsonschema.validate(instance=object, schema=schema) except jsonschema.exceptions.ValidationError as json_ve: - print("ERROR: {0} at:\n\t{1}".format(json.dumps(json_ve.message), manifest_file)) - print("\tAffected Object: {}".format(json.dumps(json_ve.instance))) + errors.append("ERROR: {0} at:\n\t{1}".format(json.dumps(json_ve.message), manifest_file)) error = True - objects[object['name']] = object + if type in objects: + objects[type].append(object) + else: + arr = [] + arr.append(object) + objects[type] = arr - # validate content - if schema_path == 'spec/v2/lookups.spec.json': - error = error or validate_lookups_content(REPO_PATH, "lookups/%s", object, manifest_file) - elif schema_path == 'spec/v2/baselines.spec.json': - error = error or validate_baselines_content(object, macros, lookups, manifest_file, baselines_uuids) - elif schema_path == 'spec/v2/story.spec.json': - error = error or validate_story_content(object, manifest_file, story_uuids) - elif schema_path == 'spec/v2/detections.spec.json': - error = error or validate_detection_content(object, macros, lookups, manifest_file, detection_uuids) - elif schema_path == 'spec/v2/investigations.spec.json': - error = error or validate_investigation_content(object, macros, lookups, manifest_file, investigation_uuids) + return objects, error, errors - if return_objects: - return error, objects + +def validate_objects(REPO_PATH, objects): + + # uuids + uuids = [] + errors = [] + + for lookup in objects['lookups']: + lookup_errors = validate_lookups_content(REPO_PATH, "lookups/%s", lookup) + + objects_array = objects['stories'] + objects['detections'] + objects['baselines'] + objects['response_tasks'] + objects['responses'] + for object in objects_array: + validation_errors, uuids = validate_standard_fields(object, uuids) + errors = errors + validation_errors + + for object in objects['detections']: + errors = errors + validate_detection_search(object, objects['macros']) + + for object in objects['baselines']: + errors = errors + validate_baseline_search(object, objects['macros']) + + errors = lookup_errors + errors + + return errors + + +def validate_standard_fields(object, uuids): + + errors = [] + + if object['id'] == '': + errors.append('ERROR: Blank ID for object: %s' % object['name']) + + if object['id'] in uuids: + errors.append('ERROR: Duplicate UUID found for object: %s' % object['name']) else: - return error + uuids.append(object['id']) + + # if object['name'].endswith(" "): + # errors.append( + # "ERROR: name has trailing spaces: '%s'" % + # object['name']) + + invalidChars = set(string.punctuation.replace("-", "")) + if any(char in invalidChars for char in object['name']): + errors.append('ERROR: No special characters allowed in name for object: %s' % object['name']) + + try: + object['description'].encode('ascii') + except UnicodeEncodeError: + errors.append("ERROR: description not ascii for object: %s" % object['name']) + + if 'how_to_implement' in object: + try: + object['how_to_implement'].encode('ascii') + except UnicodeEncodeError: + errors.append('ERROR: how_to_implement not ascii for object: %s' % object['name']) + + try: + datetime.datetime.strptime(object['date'], '%Y-%m-%d') + except ValueError: + errors.append("ERROR: Incorrect date format, should be YYYY-MM-DD for object: %s" % object['name']) + + return errors, uuids -def validate_lookups_content(REPO_PATH, lookup_path, lookup, manifest_file): - error = False +def validate_detection_search(object, macros): + errors = [] + + if not '_filter' in object['search']: + errors.append("ERROR: Missing filter for detection: " + object['name']) + + filter_macro = re.search("([a-z0-9_]*_filter)", object['search']) + if filter_macro.group(1) != (object['name'].replace(' ', '_').replace('-', '_').replace('.', '_').replace('/', '_').lower() + '_filter'): + errors.append("ERROR: filter for detection: " + object['name'] + " needs to use the name of the detection in lowercase and the special characters needs to be converted into _ .") + + if any(x in object['search'] for x in ['eventtype=', 'sourcetype=', ' source=', 'index=']): + if not 'index=_internal' in object['search']: + errors.append("ERROR: Use source macro instead of eventtype, sourcetype, source or index in detection: " + object['name']) + + macros_found = re.findall('\`([^\s]+)`',object['search']) + macros_filtered = [] + for macro in macros_found: + if not '_filter' in macro and not 'security_content_ctime' in macro and not 'drop_dm_object_name' in macro and not 'cim_' in macro and not 'get_' in macro: + macros_filtered.append(macro) + + for macro in macros_filtered: + found_macro = False + for macro_obj in macros: + if macro_obj['name'] == macro: + found_macro = True + + if not found_macro: + errors.append("ERROR: macro definition for " + macro + " can't be found for detection " + object['name']) + + return errors + +def validate_baseline_search(object, macros): + errors = [] + + if any(x in object['search'] for x in ['eventtype=', 'sourcetype=', ' source=', 'index=']): + if not 'index=_internal' in object['search']: + errors.append("ERROR: Use source macro instead of eventtype, sourcetype, source or index in detection: " + object['name']) + + macros_found = re.findall('\`([^\s]+)`',object['search']) + macros_filtered = [] + for macro in macros_found: + if not '_filter' in macro and not 'security_content_ctime' in macro and not 'drop_dm_object_name' in macro and not 'cim_' in macro and not 'get_' in macro: + macros_filtered.append(macro) + + for macro in macros_filtered: + found_macro = False + for macro_obj in macros: + if macro_obj['name'] == macro: + found_macro = True + + if not found_macro: + errors.append("ERROR: macro definition for " + macro + " can't be found for detection " + object['name']) + + return errors + + +def validate_lookups_content(REPO_PATH, lookup_path, lookup): + errors = [] if 'filename' in lookup: lookup_csv_file = path.join(path.expanduser(REPO_PATH), lookup_path % lookup['filename']) if not path.isfile(lookup_csv_file): - print("ERROR: filename {} does not exist".format(lookup['filename'])) - print(lookup_csv_file) - print("\t{}".format(manifest_file)) - error = True - - return error - - -def validate_baselines_content(baseline, macros, lookups, baselines_manifest_file, baselines_uuids): - errors = [] - error = False - - baselines_errors = validate_single_baseline_content(baseline, baselines_uuids, errors, macros, lookups) - if baselines_errors: - error = True - for err in baselines_errors: - print("{0} at:\n\t {1}".format(err, baselines_manifest_file)) - - return error - - -def validate_single_baseline_content(baseline, baselines_uuids, errors, macros, lookups): - - if baseline['id'] == '': - errors.append('ERROR: Blank ID') - - if baseline['id'] in baselines_uuids: - errors.append('ERROR: Duplicate UUID found: %s' % baseline['id']) - else: - baselines_uuids.append(baseline['id']) - - if baseline['name'].endswith(" "): - errors.append( - "ERROR: Investigation name has trailing spaces: '%s'" % - baseline['name']) - - try: - baseline['description'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: description not ascii") - - if 'how_to_implement' in baseline: - try: - baseline['how_to_implement'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: how_to_implement not ascii") - - if 'eli5' in baseline: - try: - baseline['eli5'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: eli5 not ascii") - - if 'known_false_positives' in baseline: - try: - baseline['known_false_positives'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: known_false_positives not ascii") - - if 'splunk' in baseline['baseline']: - - # do a regex match here instead of key values - if (baseline['baseline']['splunk']['search'].find('tstats') != -1) or \ - (baseline['baseline']['splunk']['search'].find('datamodel') != -1): - - if 'data_models' not in baseline['data_metadata']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' field is not set") - - if not baseline['data_metadata']['data_models']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' is empty") - - # do a regex match here instead of key values - if (baseline['baseline']['splunk']['search'].find('sourcetype') != -1): - if 'data_sourcetypes' not in baseline['data_metadata']: - errors.append("ERROR: The Splunk search specifies a sourcetype but 'data_sourcetypes' \ - field is not set") - - if not baseline['data_metadata']['data_sourcetypes']: - errors.append("ERROR: The Splunk search specifies a sourcetype but \ - 'data_sourcetypes' is empty") - - if 'macros' in baseline['baseline']['splunk']: - for macro in baseline['baseline']['splunk']['macros']: - if macro not in macros: - errors.append("ERROR: The Splunk search specifies a macro \"{}\" \ - but there is no macro manifest for it".format(macro)) - - if 'lookups' in baseline['baseline']['splunk']: - for lookup in baseline['baseline']['splunk']['lookups']: - if lookup not in lookups: - errors.append("ERROR: The Splunk search specifies a lookup \"{}\" \ - but there is no lookup manifest for it".format(lookup)) - - return errors - - -def validate_story_content(story, story_manifest_file, story_uuids): - error = False - - story_errors = validate_single_story_content(story, story_uuids) - if story_errors: - error = True - for err in story_errors: - print("{0} at:\n\t {1}".format(err, story_manifest_file)) - - return error - - -def validate_single_story_content(story, STORY_UUIDS): - ''' Validate that the content of a story manifest is correct''' - errors = [] - - if story['id'] == '': - errors.append('ERROR: Blank ID') - - if story['id'] in STORY_UUIDS: - errors.append('ERROR: Duplicate UUID found: %s' % story['id']) - else: - STORY_UUIDS.append(story['id']) - - try: - story['description'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: description not ascii") - - try: - story['narrative'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: narrative not ascii") - - return errors - - -def validate_detection_content(detection, macros, lookups, manifest_file, detection_uuids): - error = False - - detection_errors = validate_single_detection_content(detection, detection_uuids, macros, lookups) - if detection_errors: - error = True - for err in detection_errors: - print("{0} at:\n\t {1}".format(err, manifest_file)) - - return error - - -def validate_single_detection_content(detection, DETECTION_UUIDS, macros, lookups): - - errors = [] - - if detection['id'] == '': - errors.append('ERROR: Blank ID') - - if detection['id'] in DETECTION_UUIDS: - errors.append('ERROR: Duplicate UUID found: %s' % detection['id']) - else: - DETECTION_UUIDS.append(detection['id']) - - if detection['name'].endswith(" "): - errors.append( - "ERROR: Detection name has trailing spaces: '%s'" % - detection['name']) - - try: - detection['description'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: description not ascii") - - if 'how_to_implement' in detection: - try: - detection['how_to_implement'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: how_to_implement not ascii") - - if 'eli5' in detection: - try: - detection['eli5'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: eli5 not ascii") - - if 'known_false_positives' in detection: - try: - detection['known_false_positives'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: known_false_positives not ascii") - # modded to pass validation for uba detections - not yet fleshed out - if 'splunk' in detection['detect']: - # do a regex match here instead of key values - # if (detection['detect']['splunk']['correlation_rule']['search'].find('tstats') != -1) or \ - # (detection['detect']['splunk']['correlation_rule']['search'].find('datamodel') != -1): - if (detection['detect']['splunk']['correlation_rule']['search'].find('datamodel') != -1): - if 'data_models' not in detection['data_metadata']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' field is not set") - - if not detection['data_metadata']['data_models']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' is empty") - - # do a regex match here instead of key values - if (detection['detect']['splunk']['correlation_rule']['search'].find('sourcetype') != -1): - if 'data_sourcetypes' not in detection['data_metadata']: - errors.append("ERROR: The Splunk search specifies a sourcetype but 'data_sourcetypes' field is not set") - elif not detection['data_metadata']['data_sourcetypes']: - errors.append("ERROR: The Splunk search specifies a sourcetype but 'data_sourcetypes' is empty") - - if 'macros' in detection['detect']['splunk']['correlation_rule']: - for macro in detection['detect']['splunk']['correlation_rule']['macros']: - if macro not in macros: - errors.append("ERROR: The Splunk search specifies a macro \"{}\" \ - but there is no macro manifest for it".format(macro)) - - if 'lookups' in detection['detect']['splunk']['correlation_rule']: - for lookup in detection['detect']['splunk']['correlation_rule']['lookups']: - if lookup not in lookups: - errors.append("ERROR: The Splunk search specifies a lookup \"{}\" \ - but there is no lookup manifest for it".format(lookup)) - - if 'notable' in detection['detect']['splunk']['correlation_rule']: - if ('drilldown_search' in detection['detect']['splunk']['correlation_rule']['notable']) ^ \ - ('drilldown_name' in detection['detect']['splunk']['correlation_rule']['notable']): - - errors.append("ERROR: Both drilldown_search and drilldown_name must be defined") - - elif 'uba' in detection['detect']: - if (detection['detect']['uba']['correlation_rule']['search'].find('tstats') != -1) or \ - (detection['detect']['splunk']['correlation_rule']['search'].find('datamodel') != -1): - - if 'data_models' not in detection['data_metadata']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' field is not set") - - if not detection['data_metadata']['data_models']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' is empty") - - # do a regex match here instead of key values - if (detection['detect']['uba']['correlation_rule']['search'].find('sourcetype') != -1): - if 'data_sourcetypes' not in detection['data_metadata']: - errors.append("ERROR: The Splunk search specifies a sourcetype but 'data_sourcetypes' \ - field is not set") - - if not detection['data_metadata']['data_sourcetypes']: - errors.append("ERROR: The Splunk search specifies a sourcetype but \ - 'data_sourcetypes' is empty") - - # do a regex match here instead of key values - - return errors - - -def validate_investigation_content(investigation, macros, lookups, manifest_file, investigation_uuids): - error = False - - investigation_errors = validate_single_investigation_content(investigation, investigation_uuids, macros, lookups) - if investigation_errors: - error = True - for err in investigation_errors: - print("{0} at:\n\t {1}".format(err, manifest_file)) - - return error - - -def validate_single_investigation_content(investigation, investigation_uuids, macros, lookups): - errors = [] - - if investigation['id'] == '': - errors.append('ERROR: Blank ID') - - if investigation['id'] in investigation_uuids: - errors.append('ERROR: Duplicate UUID found: %s' % investigation['id']) - else: - investigation_uuids.append(investigation['id']) - - if investigation['name'].endswith(" "): - errors.append( - "ERROR: Investigation name has trailing spaces: '%s'" % - investigation['name']) - - try: - investigation['description'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: description not ascii") - - if 'how_to_implement' in investigation: - try: - investigation['how_to_implement'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: how_to_implement not ascii") - - if 'eli5' in investigation: - try: - investigation['eli5'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: eli5 not ascii") - - if 'known_false_positives' in investigation: - try: - investigation['known_false_positives'].encode('ascii') - except UnicodeEncodeError: - errors.append("ERROR: known_false_positives not ascii") - - if 'splunk' in investigation['investigate']: - - # do a regex match here instead of key values - if (investigation['investigate']['splunk']['search'].find('tstats') != -1) or \ - (investigation['investigate']['splunk']['search'].find('datamodel') != -1): - - if 'data_models' not in investigation['data_metadata']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' field is not set") - - if not investigation['data_metadata']['data_models']: - errors.append("ERROR: The Splunk search uses a data model but 'data_models' is empty") - - # do a regex match here instead of key values - if (investigation['investigate']['splunk']['search'].find('sourcetype') != -1): - if 'data_sourcetypes' not in investigation['data_metadata']: - errors.append("ERROR: The Splunk search specifies a sourcetype but 'data_sourcetypes' \ - field is not set") - - if not investigation['data_metadata']['data_sourcetypes']: - errors.append("ERROR: The Splunk search specifies a sourcetype but \ - 'data_sourcetypes' is empty") - - if 'macros' in investigation['investigate']['splunk']: - for macro in investigation['investigate']['splunk']['macros']: - if macro not in macros: - errors.append("ERROR: The Splunk search specifies a macro \"{}\" \ - but there is no macro manifest for it".format(macro)) - - if 'lookups' in investigation['investigate']['splunk']: - for lookup in investigation['investigate']['splunk']['lookups']: - if lookup not in lookups: - errors.append("ERROR: The Splunk search specifies a lookup \"{}\" \ - but there is no lookup manifest for it".format(lookup)) + errors.append("ERROR: filename {} does not exist".format(lookup['filename'])) return errors @@ -424,16 +202,26 @@ if __name__ == "__main__": REPO_PATH = args.path verbose = args.verbose - macros_error, macros = validate_object(REPO_PATH, 'spec/v2/macros.spec.json', 'macros/*.yml', True, verbose) - lookups_error, lookups = validate_object(REPO_PATH, 'spec/v2/lookups.spec.json', 'lookups/*.yml', True, verbose) - story_error = validate_object(REPO_PATH, 'spec/v2/story.spec.json', 'stories/*.yml', False, verbose) - detection_error = validate_object(REPO_PATH, 'spec/v2/detections.spec.json', 'detections/*.yml', False, - verbose, lookups, macros) - investigation_error = validate_object(REPO_PATH, 'spec/v2/investigations.spec.json', 'investigations/*.yml', - False, verbose, lookups, macros) - baseline_error = validate_object(REPO_PATH, 'spec/v2/baselines.spec.json', 'baselines/*.yml', False, verbose, lookups, macros) + validation_objects = ['macros','lookups','stories','detections','baselines','response_tasks','responses','deployments'] - if story_error or detection_error or investigation_error or baseline_error or macros_error or lookups_error: + objects = {} + schema_error = False + schema_errors = [] + + for validation_object in validation_objects: + objects, error, errors = validate_schema(REPO_PATH, validation_object, objects) + schema_error = schema_error or error + if len(errors) > 0: + schema_errors = schema_errors + errors + + validation_errors = validate_objects(REPO_PATH, objects) + + schema_errors = schema_errors + validation_errors + + for schema_error in schema_errors: + print(schema_error) + + if schema_error or len(schema_errors) > 0: sys.exit("Errors found") else: print("No Errors found") diff --git a/dashboards/.gitkeep b/dashboards/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deployments/enterprise_security_deployment_configuration.yml b/deployments/enterprise_security_deployment_configuration.yml new file mode 100644 index 0000000000..8305cd78a9 --- /dev/null +++ b/deployments/enterprise_security_deployment_configuration.yml @@ -0,0 +1,20 @@ +name: Enterprise Security deployment configuration +id: bc91a8cd-35e7-4bb2-6140-e756cc46f212 +date: '2020-04-27' +description: This configuration file applies to all correlation searches that are used for detection +author: Bhavin Patel +scheduling: + cron_schedule: '*/30 * * * *' + earliest_time: -30m + latest_time: now + schedule_window: auto +alert_action: + notable: + rule_description: '%description%' + rule_title: '%name%' + nes_fields: + - user + - dest + - src +tags: + analytics_story: all diff --git a/detections/abnormally_high_aws_instances_launched_by_user.yml b/detections/abnormally_high_aws_instances_launched_by_user.yml new file mode 100644 index 0000000000..c53af3d951 --- /dev/null +++ b/detections/abnormally_high_aws_instances_launched_by_user.yml @@ -0,0 +1,37 @@ +name: Abnormally High AWS Instances Launched by User +id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4 +version: 1 +date: '2018-02-26' +description: This search looks for CloudTrail events where a user successfully launches + an abnormally high number of instances. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. The threshold value should be tuned to your environment. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time + | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) + as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval + threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev + * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), + "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) + / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, + total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter`' +known_false_positives: Many service accounts configured within an AWS infrastructure + are known to exhibit this behavior. Please adjust the threshold values and filter + out service accounts from the output. Always verify if this search alerted on a + human user. +tags: + analytics_story: + - AWS Cryptomining + - Suspicious AWS EC2 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml b/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml new file mode 100644 index 0000000000..41a777f6b4 --- /dev/null +++ b/detections/abnormally_high_aws_instances_launched_by_user___mltk.yml @@ -0,0 +1,33 @@ +name: Abnormally High AWS Instances Launched by User - MLTK +id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5 +version: 1 +date: '2019-11-14' +description: This search looks for CloudTrail events where a user successfully launches + an abnormally high number of instances. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. The threshold value should be tuned to your environment. +type: ESCU +references: [] +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` + | bucket span=10m _time | stats count as instances_launched by _time src_user | + apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as + isOutlier | where isOutlier=1' +known_false_positives: Many service accounts configured within an AWS infrastructure + are known to exhibit this behavior. Please adjust the threshold values and filter + out service accounts from the output. Always verify if this search alerted on a + human user. +tags: + analytics_story: + - Cloud Cryptomining + - Suspicious AWS EC2 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/abnormally_high_aws_instances_terminated_by_user.yml b/detections/abnormally_high_aws_instances_terminated_by_user.yml new file mode 100644 index 0000000000..bac36b30da --- /dev/null +++ b/detections/abnormally_high_aws_instances_terminated_by_user.yml @@ -0,0 +1,37 @@ +name: Abnormally High AWS Instances Terminated by User +id: ada0f478-84a8-4641-s3f3-d82362dffd75 +version: 1 +date: '2018-02-26' +description: This search looks for CloudTrail events where an abnormally high number + of instances were successfully terminated by a user in a 10-minute window +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m + _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) + as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev + | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev + * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), + "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - + total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, + num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | + `abnormally_high_aws_instances_terminated_by_user_filter`' +known_false_positives: Many service accounts configured with your AWS infrastructure + are known to exhibit this behavior. Please adjust the threshold values and filter + out service accounts from the output. Always verify whether this search alerted + on a human user. +tags: + analytics_story: + - Suspicious AWS EC2 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml b/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml new file mode 100644 index 0000000000..94c4b059e3 --- /dev/null +++ b/detections/abnormally_high_aws_instances_terminated_by_user___mltk.yml @@ -0,0 +1,32 @@ +name: Abnormally High AWS Instances Terminated by User - MLTK +id: 1c02b86a-cd85-473e-a50b-014a9ac8fe3e +version: 1 +date: '2019-11-14' +description: This search looks for CloudTrail events where a user successfully terminates + an abnormally high number of instances. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. The threshold value should be tuned to your environment. +type: ESCU +references: [] +author: Jason Brewer, Splunk +search: '`cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` + | bucket span=10m _time | stats count as instances_terminated by _time src_user | + apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" + as isOutlier | where isOutlier=1' +known_false_positives: Many service accounts configured within an AWS infrastructure + are known to exhibit this behavior. Please adjust the threshold values and filter + out service accounts from the output. Always verify if this search alerted on a + human user. +tags: + analytics_story: + - Suspicious AWS EC2 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/abnormally_high_ec2_instances_launched.yml b/detections/abnormally_high_ec2_instances_launched.yml deleted file mode 100644 index ae6a597344..0000000000 --- a/detections/abnormally_high_ec2_instances_launched.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: AWS Instance -confidence: medium -creation_date: '2018-02-26' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user successfully launches - an abnormally high number of instances. -detect: - splunk: - correlation_rule: - notable: - nes_fields: userName - rule_description: An abnormally high number of instances were launched by - a user within in a 10-minute window - rule_title: High Number of instances launched by $userName$ - risk: - risk_object: userName - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: '*/10 * * * *' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | - bucket span=10m _time | stats count AS instances_launched by _time userName - | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) - as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched - > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search - isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away - = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, - 2) | table _time, userName, instances_launched, num_standard_deviations_away, - total_launched_avg, total_launched_stdev - suppress: - suppress_fields: userName - suppress_period: 3600s -eli5: In this search, we query CloudTrail logs to look for events where an instance - is successfully launched by a particular user. Since we want to detect a high number - of instances launched within a short period, we create event buckets for 10-minute - windows. We then calculate the total number of instances launched by a particular - user, as well as the average and standard deviation values. Assign a `threshold_value` - in the search. Start with 3 (but it will likely need to be tweaked for your environment). - The `eval` function will set the outlier 1 if the number of instances is greater - than the average number of instances terminated, added to the multiplied value of - threshold and standard deviation. For your reference, we then keep only the outliers - and calculate the number of standard deviations away the value is from the average. -entities: - - userName -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. The threshold value should be tuned to your environment. -id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: Many service accounts configured within an AWS infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify if this search alerted on a - human user. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - DE.DP - - DE.AE -modification_date: '2018-02-26' -name: Abnormally High AWS Instances Launched by User -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/abnormally_high_instance_termination.yml b/detections/abnormally_high_instance_termination.yml deleted file mode 100644 index f06f0ca3fd..0000000000 --- a/detections/abnormally_high_instance_termination.yml +++ /dev/null @@ -1,106 +0,0 @@ -asset_type: AWS Instance -confidence: medium -creation_date: '2018-02-26' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where an abnormally high number - of instances were successfully terminated by a user in a 10-minute window -detect: - splunk: - correlation_rule: - notable: - nes_fields: userName - rule_description: An abnormally high number of instances were terminated by - a user in a 10-minute window - rule_title: High number of instances terminated by $userName$ - risk: - risk_object: userName - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: '*/10 * * * *' - earliest_time: -30d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success - | bucket span=10m _time | stats count AS instances_terminated by _time userName - | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) - as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated - > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, - 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval - num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) - / total_terminations_stdev, 2) |table _time, userName, instances_terminated, - num_standard_deviations_away, total_terminations_avg, total_terminations_stdev - suppress: - suppress_fields: userName - suppress_period: 3600s -eli5: In this search, we query CloudTrail logs to look for events where an instance - is successfully terminated by a particular user. Since we want to detect a high - number of instances terminated within a short period, we create event buckets for - 10-minute windows. We then calculate the total number of instances terminated by - a particular user, as well as the average- and standard-deviation values. Assign - a `threshold_value` in the search. Try starting with 3 (but it will likely need - to be tweaked for your environment). The `eval` function will set the outlier to - 1 if the number of instances is greater than the average number of instances terminated, - added to the multiplied value of threshold and standard deviation. We then filter - out outliers with a value of 1 and show only those instance-termination events that - happened within the previous 10 minutes. -entities: - - userName -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: ada0f478-84a8-4641-s3f3-d82362dffd75 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: Many service accounts configured with your AWS infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify whether this search alerted - on a human user. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - DE.DP - - DE.AE -modification_date: '2018-02-26' -name: Abnormally High AWS Instances Terminated by User -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/access_lsass_memory_for_dump_creation.yml b/detections/access_lsass_memory_for_dump_creation.yml index 17fad65794..0ee95954cb 100644 --- a/detections/access_lsass_memory_for_dump_creation.yml +++ b/detections/access_lsass_memory_for_dump_creation.yml @@ -1,88 +1,35 @@ -asset_type: Windows -confidence: high -creation_date: '2019-12-06' -data_metadata: - data_eventtypes: - - wineventlog_sysmon - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: Detect memory dumping of the LSASS process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Possible attempt at credential dumping was detected on $dest$. - rule_title: LSASS memory dump detected on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - macros: - - sysmon - - access_lsass_memory_for_dump_creation_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* - | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId - | rename Computer as dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `access_lsass_memory_for_dump_creation_filter` ' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. - This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process - can be created with tools such as Windows Task Manager or procdump. -entities: - - dest -how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 6 - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.CM -modification_date: '2019-12-06' name: Access LSASS Memory for Dump Creation -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717 +version: 2 +date: '2019-12-06' +description: Detect memory dumping of the LSASS process. +how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which + includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +type: ESCU references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: "2" +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* + | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, + TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` ' +known_false_positives: Administrators can create memory dumps for debugging purposes, + but memory dumps of the LSASS process would be unusual. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 6 + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/account_harvesting.yml b/detections/account_harvesting.yml deleted file mode 100644 index e2b52a00dc..0000000000 --- a/detections/account_harvesting.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: Account -confidence: medium -creation_date: '2018-07-12' -data_metadata: - data_source: - - Network Communications - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search is used to identify the creation of multiple user accounts - using the same email domain name. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_user - rule_description: This search is used to identify multiple created accounts - tied to a specific email domain. Such activity is often indicative of account - harvesting. A list of $src_user$ accounts were created. - rule_title: 'Web Fraud Detection: Possible Account Harvesting' - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 40 - schedule: - cron_schedule: 0 1 * * * - earliest_time: -1445m@m - latest_time: -10m@m - search: sourcetype=stream:http http_content_type=text* uri="/magento2/customer/account/loginPost/" - | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" - | search Username=* | rex field=Username "@(?.*)"|stats dc(Username) - as UniqueUsernames list(Username) as src_user by email_domain|where UniqueUsernames> - 25 - suppress: - suppress_fields: user - suppress_period: 3600s -eli5: When a fraudster is setting the stage for a campaign, they will often create - many user accounts on the website. This is a simple example of how to detect a many-account - creation hosted on a Magento2 e-commerce platform, where the fraudster is using - email addresses from a single email domain. -entities: - - src_user -how_to_implement: We start with a dataset that provides visibility into the email - address used for the account creation. In this example, we are narrowing our search - down to the single web page that hosts the Magento2 e-commerce platform (via URI) - used for account creation, the single http content-type to grab only the user's - clicks, and the http field that provides the username (form_data), for performance - reasons. After we have the username and email domain, we look for numerous account - creations per email domain. Common data sources used for this detection are customized - Apache logs or Splunk Stream. -id: 31337aaa-941d-4ada-81ac-q2a17be5bf0d -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89 - name: Get Web Session Information via session_id - type: splunk - - id: 5df39b3f-447d-4869-b673-8f45ad4616fe - name: Get Emails From Specific Sender - type: splunk -known_false_positives: As is common with many fraud-related searches, we are usually - looking to attribute risk or synthesize relevant context with loosely written detections - that simply detect anamolous behavior. This search will need to be customized to - fit your environment—improving its fidelity by counting based on something - much more specific, such as a device ID that may be present in your dataset. Consideration - for whether the large number of registrations are occuring from a first-time seen - domain may also be important. Extending the search window to look further back - in time, or even calculating the average per hour/day for each email domain to look - for an anomalous spikes, will improve this search. You can also use Shannon entropy - or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness - or similarity of the email name or email domain, as the names are often machine-generated. -maintainers: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Create Account - nist: - - DE.CM - - DE.DP -modification_date: '2018-10-08' -name: Web Fraud - Account Harvesting -original_authors: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -references: - - https://splunkbase.splunk.com/app/2734/ - - https://splunkbase.splunk.com/app/1809/ -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/acl_with_open_ports.yml b/detections/acl_with_open_ports.yml deleted file mode 100644 index 1cadc3ca57..0000000000 --- a/detections/acl_with_open_ports.yml +++ /dev/null @@ -1,95 +0,0 @@ -asset_type: AWS Instance -confidence: medium -creation_date: '2017-01-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: The search looks for CloudTrail events to detect if any network ACLs - were created with all the ports open to a specified CIDR. -detect: - splunk: - correlation_rule: - notable: - nes_fields: aws_account_id, src, arn - rule_description: $src_user$ created a network access control list with all - ports open. - rule_title: Network ACL created with all ports open by $src_user$ - risk: - risk_object: arn - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -1d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=CreateNetworkAclEntry | mvexpand - requestParameters | mvexpand responseElements | search requestParameters.portRange.from=1024 - requestParameters.portRange.to=65535 requestParameters.ruleAction=allow | - rename userIdentity.arn as arn | rename requestParameters.networkAclId as - networkAclId | table _time aws_account_id src userName arn networkAclId requestParameters.* - responseElements.* - suppress: - suppress_fields: arn - suppress_period: 14400s -eli5: A network access control list (ACL) is a layer of security for your VPC that - acts as a firewall for controlling traffic in and out of one or more subnets. Network - ACLs with all open ports have a larger attack surface. This search looks for events - within your CloudTrail logs to check if there were any Network ACLs created with - ports ranging from 1024 to 65525. This search will create a table comprised of AWS - account id, src, user and all parameters of the request made by the user and the - server response. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail - inputs. -id: ada0f478-84a8-4641-a3f1-d82362d6bd75 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242 - name: AWS Network ACL Details from ID - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible that an admin has created this ACL with all ports - open for some legitimate purpose however, this should be scoped and not allowed - in production environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 11 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - nist: - - DE.DP - - DE.AE -modification_date: '2017-01-10' -name: AWS Network Access Control List Created with All Open Ports -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/activity_related_to_pass_the_hash.yml b/detections/activity_related_to_pass_the_hash.yml deleted file mode 100644 index 5169d8b2df..0000000000 --- a/detections/activity_related_to_pass_the_hash.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2016-09-13' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Authentication - providing_technologies: - - Microsoft Windows -description: This search looks for specific authentication events from the Windows - Security Event logs to detect potential attempts at using the Pass-the-Hash technique. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest, user - rule_description: This search looks for Authentication log events from the - Windows Security Audit logs to detect potential attempts for Passing the - Hash - rule_title: Detect Activity Related to Pass the Hash - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 10 - macros: - - wineventlog_security - - detect_activity_related_to_pass_the_hash_attacks_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`wineventlog_security` EventCode=4624 (Logon_Type=3 LogonProcessName=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR - (EventCode=4624 Logon_Type=9 LogonProcessName=seclogo) - | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_activity_related_to_pass_the_hash_attacks_filter` ' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: To detect pass the hash activity, we look at all events with event code 4624 - that specify a logon type 3 (network logons) for remote pass the hash attacks and - logon type 9 for local pass the hash attacks. The search also filters out - events with an account name of 'Anonymous' to help reduce false positives. -entities: - - dest -how_to_implement: To successfully implement this search, you must ingest your Windows - Security Event logs and leverage the latest TA for Windows. -id: f5939373-8054-40ad-8c64-cec478a22a4b -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Legitimate logon activity by authorized NTLM systems may be - detected by this search. Please investigate as appropriate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - - Pass the Hash - mitre_technique_id: - - T1075 - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2020-01-09' -name: Detect Activity Related to Pass the Hash Attacks -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/add_to_untrust_cert_store.yml b/detections/add_to_untrust_cert_store.yml deleted file mode 100644 index df0f0b47c8..0000000000 --- a/detections/add_to_untrust_cert_store.yml +++ /dev/null @@ -1,115 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-04-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Attempt to add a certificate to the untrusted certificate store -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: Attempt to add a certificate to the untrusted certificate - store - rule_title: Attempt To Add Certificate to Untrusted Store - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* - ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`' - suppress: - suppress_fields: process, dest - suppress_period: 86400s -eli5: Attackers will often attempt to disable security tools in order to evade detection. - It is also possible for end users to attempt to disable anti-virus or other security - tools to circumvent restrictions they encounter while trying to execute other programs. - One way malware may accomplish this is by adding the legitimate certificate used - to sign the security software to the untrusted certificate store. This will cause - the system to no longer trust the software signed with this certificate and disallow - it from executing. This search simply looks for the execution of **certutil.exe** - with the parameters `-addcert` and `disallowed`, which add a certification to the - "untrusted" certificate store. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: 6bc5243e-ef36-45dc-9b12-f4a6be131159 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There may be legitimate reasons for administrators to add a - certificate to the untrusted certificate store. In such cases, this will typically - be done on a large number of systems. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 8 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Disabling Security Tools - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2018-11-15' -name: Attempt To Add Certificate To Untrusted Store -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' diff --git a/detections/amazon_eks_kubernetes_cluster_scan_detection.yml b/detections/amazon_eks_kubernetes_cluster_scan_detection.yml new file mode 100644 index 0000000000..c226e5ee2f --- /dev/null +++ b/detections/amazon_eks_kubernetes_cluster_scan_detection.yml @@ -0,0 +1,27 @@ +name: Amazon EKS Kubernetes cluster scan detection +id: 294c4686-63dd-4fe6-93a2-ca807626704a +version: 1 +date: '2020-04-15' +description: This search provides information of unauthenticated requests via user + agent, and authentication data against Kubernetes cluster in AWS +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch + EKS Logs inputs. +type: ESCU +references: [] +author: Rod Soto, Splunk +search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS + Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime + max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name + values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) + by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + |`amazon_eks_kubernetes_cluster_scan_detection_filter` ' +known_false_positives: Not all unauthenticated requests are malicious, but frequency, + UA and source IPs will provide context. +tags: + analytics_story: + - Kubernetes Scanning Activity + kill_chain_phases: + - Reconnaissance + security_domain: threat + asset_type: Amazon EKS Kubernetes cluster diff --git a/detections/amazon_eks_kubernetes_pod_scan_detection.yml b/detections/amazon_eks_kubernetes_pod_scan_detection.yml new file mode 100644 index 0000000000..846074fa72 --- /dev/null +++ b/detections/amazon_eks_kubernetes_pod_scan_detection.yml @@ -0,0 +1,28 @@ +name: Amazon EKS Kubernetes Pod scan detection +id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002 +version: 1 +date: '2020-04-15' +description: This search provides detection information on unauthenticated requests + against Kubernetes' Pods API +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch + EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` + macro to filter out the false positives. +type: ESCU +references: [] +author: Rod Soto, Splunk +search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods + requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip + | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) + values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by + src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` ' +known_false_positives: Not all unauthenticated requests are malicious, but frequency, + UA and source IPs and direct request to API provide context. +tags: + analytics_story: + - Kubernetes Scanning Activity + kill_chain_phases: + - Reconnaissance + security_domain: threat + asset_type: Amazon EKS Kubernetes cluster Pod diff --git a/detections/anomalous_webclick.yml b/detections/anomalous_webclick.yml deleted file mode 100644 index e093a80b6e..0000000000 --- a/detections/anomalous_webclick.yml +++ /dev/null @@ -1,101 +0,0 @@ -asset_type: account -confidence: medium -creation_date: '2018-07-12' -data_metadata: - data_source: - - Network Communications - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search is used to examine web sessions to identify those where the - clicks are occurring too quickly for a human or are occurring with a near-perfect - cadence (high periodicity or low standard deviation), resembling a script driven - session. -detect: - splunk: - correlation_rule: - notable: - nes_fields: session_id - rule_description: This search is used to examine web sessions in order to - identify unnaturally rapid clicks with near-perfect cadence (high periodicity - or low standard deviation), which resemble a script-driven session. - rule_title: 'Web Fraud Detection: Anomalous User Clickspeed' - risk: - risk_object: session_id - risk_object_type: - - other - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=stream:http http_content_type=text* | rex field=cookie "form_key=(?\w+)" - | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | - where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) - as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR - ClickSpeedAvg<.5) - suppress: - suppress_fields: session_id - suppress_period: 3600s -eli5: It's suspicious when someone or something is moving throughout your website - too quickly or with a perfect click cadence. Fortunately, it's easy to detect by - calculating the time between clicks for each session and highlighting the anomalous - behavior. -entities: - - session_id -how_to_implement: Start with a dataset that allows you to see clickstream data for - each user click on the website. That data must have a time stamp and must contain - a reference to the session identifier being used by the website. This ties the clicks - together into clickstreams. This value is usually found in the http cookie. With - a bit of tuning, a version of this search could be used in high-volume scenarios, - such as scraping, crawling, application DDOS, credit-card testing, account takeover, - etc. Common data sources used for this detection are customized Apache logs, customized - IIS, and Splunk Stream. -id: 31337bbb-bc22-4752-b599-ef192df2dc7a -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89 - name: Get Web Session Information via session_id - type: splunk - - id: 5df39b3f-447d-4869-b673-8f45ad4616fe - name: Get Emails From Specific Sender - type: splunk -known_false_positives: As is common with many fraud-related searches, we are usually - looking to attribute risk or synthesize relevant context with loosly written detections - that simply detect anamoluous behavior. -maintainers: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -mappings: - cis20: - - CIS 6 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Initial Access - - Valid Accounts - nist: - - DE.AE - - DE.CM -modification_date: '2018-10-08' -name: Web Fraud - Anomalous User Clickspeed -original_authors: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -references: - - https://en.wikipedia.org/wiki/Session_ID - - https://en.wikipedia.org/wiki/Session_(computer_science) - - https://en.wikipedia.org/wiki/HTTP_cookie - - https://splunkbase.splunk.com/app/1809/ -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/api_acitivity_from_previously_unseen_user_role.yml b/detections/api_acitivity_from_previously_unseen_user_role.yml deleted file mode 100644 index e356065607..0000000000 --- a/detections/api_acitivity_from_previously_unseen_user_role.yml +++ /dev/null @@ -1,97 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-fq2c-48b0-9f6f-63da3289fd03 - name: Previously seen API call per user roles in CloudTrail - type: splunk -confidence: medium -creation_date: '2018-04-01' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search detects new API calls that have either never been seen before - or that have not been seen in the previous hour, where the identity type is `AssumedRole`. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A new API call made by $user$ has been detected. This API - activity has either never been seen before or has not been seen within the - last hour. - rule_title: New API call by $user$ detected - risk: - risk_object: user - risk_object_type: - - user - risk_score: 10 - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole - [search sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole - | stats earliest(_time) as earliest latest(_time) as latest by userName eventName - | inputlookup append=t previously_seen_api_calls_from_user_roles | stats - min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup - previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), - "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` - | table eventName userName] |rename userName as user| stats values(eventName) - earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` - | `security_content_ctime(latest)` - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: The subsearch will execute first and return the user roles and names of the - API calls completed within the last hour, where the type of user identity is `AssumedRole`. - It then appends the historical data to those results in the lookup file. Next, it - recalculates the `earliest` and `latest` fields for each user role, as well as the - name of the API call, and returns only those roles and API calls that have first - been seen in the past hour. This is combined with the main search to return the - values of API calls, name of the user role, and the earliest and latest time of - this activity. It is worth noting that the name of the role of a particular user - is parsed as "userName" in the CloudTrail logs. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. This search works best when you run the "Previously seen API call per user - roles in CloudTrail" support search once to create a history of previously seen - user roles. -id: 22773e84-bac0-4595-b086-20d3f335b4f1 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 - name: Investigate AWS User Activities by user field - type: splunk -known_false_positives: It is possible that there are legitimate user roles making - new or infrequently used API calls in your infrastructure, causing the search to - trigger. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-04-16' -name: Detect new API calls from user roles -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/attackers_scanning_for_vulnerable_jboss_servers.yml deleted file mode 100644 index 3b780a10e2..0000000000 --- a/detections/attackers_scanning_for_vulnerable_jboss_servers.yml +++ /dev/null @@ -1,89 +0,0 @@ -asset_type: Web Server -confidence: medium -creation_date: '2016-10-04' -data_metadata: - data_models: - - Web - data_source: - - Network Communications - - Web Server - providing_technologies: - - Splunk Stream - - Palo Alto Firewall - - Apache - - Bro -description: This search looks for specific GET or HEAD requests to web servers that - are indicative of reconnaissance attempts to identify vulnerable JBoss servers. - JexBoss is described as the exploit tool of choice for this malicious activity. -detect: - splunk: - correlation_rule: - notable: - nes_fields: '' - rule_description: This search looks for specific GET/HEAD requests to web - servers that are indicative of reconnaissance attempts to identify vulnerable - JBoss servers. - rule_title: Detect attackers scanning for vulnerable JBoss servers - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") - AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR - Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, - Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,url - suppress_period: 86400s -eli5: This search returns the number of times a URL associated with this type of JexBoss - probe is observed. -entities: - - dest -how_to_implement: You must be ingesting data from the web server or network traffic - that contains web specific information, and populating the Web data model. -id: 104658f4-afdc-499e-9719-17243f982681 -investigations: - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: It's possible for legitimate HTTP requests to be made to URLs - containing the suspicious paths. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - kill_chain_phases: - - Reconnaissance - mitre_attack: - - Discovery - - System Information Discovery -modification_date: '2017-09-23' -name: Detect attackers scanning for vulnerable JBoss servers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/attempt_to_add_certificate_to_untrusted_store.yml b/detections/attempt_to_add_certificate_to_untrusted_store.yml new file mode 100644 index 0000000000..9ed273f5fc --- /dev/null +++ b/detections/attempt_to_add_certificate_to_untrusted_store.yml @@ -0,0 +1,39 @@ +name: Attempt To Add Certificate To Untrusted Store +id: 6bc5243e-ef36-45dc-9b12-f4a6be131159 +version: 4 +date: '2018-11-15' +description: Attempt to add a certificate to the untrusted certificate store +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) + as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe + (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process + Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` + | `attempt_to_add_certificate_to_untrusted_store_filter`' +known_false_positives: There may be legitimate reasons for administrators to add a + certificate to the untrusted certificate store. In such cases, this will typically + be done on a large number of systems. +tags: + analytics_story: + - Disabling Security Tools + mitre_attack_id: + - T1089 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml b/detections/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml index 0a8403bed8..96e9e04f15 100644 --- a/detections/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml +++ b/detections/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml @@ -1,95 +1,41 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-08-28' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, registry_path, registry_key_name, registry_value_name - rule_description: An attempt to modify the default PowerShell execution policy - in the registry to "Unrestricted" or "Bypass" was detected on $dest$. - rule_title: Attempt To Set PowerShell Execution Policy To "Unrestricted" or "Bypass" On - $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - macros: - - attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry - where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* - Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) - by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest - | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` ' - suppress: - suppress_fields: process_name, dest - suppress_period: 86400s -eli5: This search looks for changes of the ExecutionPolicy in the registry. The ExecutionPolicy - is a safety feature that controls the conditions under which PowerShell loads configuration - files and runs scripts. Usually, the ExecutionPolicy is "Restricted" for Windows clients and - "RemoteSigned" for Windows Servers, allowing only certain scripts to run. This search detects - when an attacker sets the ExecutionPolicy to "Unrestricted" or "Bypass." -entities: - - dest +name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +id: c2590137-0b08-4985-9ec5-6ae23d92f63d +version: 4 +date: '2019-12-02' +description: Monitor for changes of the ExecutionPolicy in the registry to the values + "unrestricted" or "bypass," which allows the execution of malicious scripts. how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be - ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -id: c2590137-0b08-4985-9ec5-6ae23d92f63d -known_false_positives: Administrators may attempt to change the default execution - policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" - as this search is designed to identify, would be unusual. Hits should be reviewed - and investigated as appropriate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 3 - - CIS 8 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_technique_id: - - T1086 - - T1064 - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - DE.CM -modification_date: '2019-12-02' -name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss + ingesting logs with the fields registry_path, registry_key_name, and registry_value_name + from your endpoints. +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' +author: Patrick Bareiss, Splunk +search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)\ + \ as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\\ + \\Microsoft\\\\Powershell\\\\1\\\\ShellIds\\\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy\ + \ (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass)\ + \ by Registry.registry_path Registry.registry_key_name Registry.registry_value_name\ + \ Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`|\ + \ `security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`\_" +known_false_positives: Administrators may attempt to change the default execution + policy on a system for a variety of reasons. However, setting the policy to "unrestricted" + or "bypass" as this search is designed to identify, would be unusual. Hits should + be reviewed and investigated as appropriate. +tags: + analytics_story: + - Malicious PowerShell + - Credential Dumping + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/attempt_to_stop_security_service.yml b/detections/attempt_to_stop_security_service.yml new file mode 100644 index 0000000000..205e49f0a0 --- /dev/null +++ b/detections/attempt_to_stop_security_service.yml @@ -0,0 +1,46 @@ +name: Attempt To Stop Security Service +id: c8e349c6-b97c-486e-8949-bd7bcd1f3910 +version: 2 +date: '2017-09-15' +description: This search looks for attempts to stop security-related services on the + endpoint. +how_to_implement: You must be ingesting data that records the file-system activity + from your hosts to populate the Endpoint file-system data-model node. If you are + using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which + you want to collect data. The search is shipped with a lookup file, `security_services.csv`, + that can be edited to update the list of services to monitor. This lookup file can + be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, + or via the Splunk console. You should add the names of services an attacker might + use on the command line and surround with asterisks (*****), so that they work properly + when searching the command line. The file should be updated with the names of any + services you would like to monitor for attempts to stop the service., +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* + stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + |lookup security_services_lookup service as process OUTPUTNEW category, description + | search category=security | `attempt_to_stop_security_service_filter`' +known_false_positives: None identified. Attempts to disable security-related services + should be identified and understood. +tags: + analytics_story: + - Disabling Security Tools + mitre_attack_id: + - T1089 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/attempted_credential_dump_from_registry_via_reg.yml b/detections/attempted_credential_dump_from_registry_via_reg.yml deleted file mode 100644 index 4a42aa101a..0000000000 --- a/detections/attempted_credential_dump_from_registry_via_reg.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-08-28' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: Monitor for execution of reg.exe with parameters specifying - an export of keys that contain hashed credentials that attackers may try to crack - offline. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, process_name, process - rule_description: An attempt to save registry keys holding credentials was - identified by $user$. - rule_title: Attempted Credential Dump From Registry from $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 80 - macros: - - attempted_credential_dump_from_registry_via_reg_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* - (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR - Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR - Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) - by Processes.user Processes.process_name Processes.process Processes.dest - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `attempted_credential_dump_from_registry_via_reg_filter`' - suppress: - suppress_fields: process_name, dest - suppress_period: 86400s -eli5: This search looks for the process reg.exe with the "save" parameter, which specifies - a binary export from the registry. In addition, it looks for the keys that contain - the hashed credentials, which attackers may retrieve and use for brute-force attacks - in order to harvest legitimate credentials. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints, to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: 14038953-e5f2-4daf-acff-5452062baf03 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: None identified. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_technique_id: - - T1003 - mitre_attack: - - Credential Access - - Credential Dumping - nist: - - DE.CM -modification_date: '2019-12-02' -name: Attempted Credential Dump From Registry via Reg.exe -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' diff --git a/detections/attempted_credential_dump_from_registry_via_reg_exe.yml b/detections/attempted_credential_dump_from_registry_via_reg_exe.yml new file mode 100644 index 0000000000..557c69ae1c --- /dev/null +++ b/detections/attempted_credential_dump_from_registry_via_reg_exe.yml @@ -0,0 +1,36 @@ +name: Attempted Credential Dump From Registry via Reg exe +id: 14038953-e5f2-4daf-acff-5452062baf03 +version: 4 +date: '2019-12-02' +description: Monitor for execution of reg.exe with parameters specifying an export + of keys that contain hashed credentials that attackers may try to crack offline. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints, to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe + OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* + OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* + OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) + by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` ' +known_false_positives: None identified. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/attrib_to_hide_files.yml b/detections/attrib_to_hide_files.yml deleted file mode 100644 index 5067c265b6..0000000000 --- a/detections/attrib_to_hide_files.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: '' -confidence: medium -creation_date: '2017-10-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Attackers leverage an existing Windows binary, attrib.exe, to mark specific - as hidden by using specific flags so that the victim does not see the file. The - search looks for specific command-line arguments to detect the use of attrib.exe - to hide files. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: Attrib.exe is often used by attackers to hide malware files - and directories in windows environments. This rule detects command-line - arguments used to hide a file/directory - rule_title: 'Suspicious usage of attrib.exe on $dest$ ' - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - attrib_to_hide_files_filter - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process - Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `attrib_to_hide_files_filter`' - suppress: - suppress_fields: dest, process - suppress_period: 86400s -eli5: This search is looking to detect command-line execution with of attrib.exe binary - with the +h flag set. The +h flag is used to hide a file. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: c77162d3-f93c-45cc-80c8-22f6b5264g9f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: 'Some applications and users may legitimately use attrib.exe - to interact with the files. ' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Persistence - nist: - - DE.CM -modification_date: '2020-03-16' -name: Hiding Files And Directories With Attrib.exe -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/aws_activity_from_non_approved_accounts.yml b/detections/aws_activity_from_non_approved_accounts.yml deleted file mode 100644 index 94ffc92da8..0000000000 --- a/detections/aws_activity_from_non_approved_accounts.yml +++ /dev/null @@ -1,118 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b1-5f6f-63ga3789fd43 - name: Create a list of approved AWS service accounts - type: splunk -confidence: medium -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for successful CloudTrail activity by user accounts - that are not listed in the identity table or `aws_service_accounts.csv`. It returns - event names and count, as well as the first and last time a specific user or service - is detected, grouped by users. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A successful API activity was invoked by $user$, an unapproved/unknown - account. - rule_title: 'Successful API activity by a non-approved account: $user$' - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail errorCode=success | rename userName as identity - | search NOT [| inputlookup identity_lookup_expanded | fields identity] | - search NOT [| inputlookup aws_service_accounts | fields identity] | rename - identity as user | stats count min(_time) as firstTime max(_time) as lastTime - values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - suppress: - suppress_fields: user - suppress_period: 14400s -eli5: In this search, we are looking for successful API calls via CloudTrail. We filter - out events triggered by known users listed in the `identity_lookup_expanded` lookup - file and the service accounts. Once filtered out, we output a table with the event - names and count, as well as the first and last time a specific user or service is - detected. -entities: - - user -how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. You must also populate the `identity_lookup_expanded` lookup shipped with - the Asset and Identity framework to be able to look up users in your identity table - in Enterprise Security (ES). Leverage the support search called "Create a list of - approved AWS service accounts": run it once every 30 days to create and validate - a list of service accounts.\ - - This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet - supported by ES Incident Review and therefore cannot be viewed when a notable event - is raised. These fields contribute additional context to the notable. To see the - additional metadata, add the following fields, if not already present, to Incident - Review - Event Attributes (Configure > Incident Management > Incident Review Settings - > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ - - 1. \ - - 1. **Label:** First Time, **Field:** firstTime\ - - 1. \ - - 1. **Label:** Last Time, **Field:** lastTime\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: ada0f478-84a8-4641-a3f1-d82362d4bd55 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 - name: Investigate AWS User Activities by user field - type: splunk -known_false_positives: It's likely that you'll find activity detected by users/service - accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` - file. If the user is a legitimate service account, update the `aws_service_accounts.csv` - table with that entry. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Execution - nist: - - DE.DP - - DE.CM - - PR.AC - - ID.AM -modification_date: '2018-03-13' -name: Detect AWS API Activities From Unapproved Accounts -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/aws_activity_in_new_region.yml b/detections/aws_activity_in_new_region.yml deleted file mode 100644 index 0285237a0a..0000000000 --- a/detections/aws_activity_in_new_region.yml +++ /dev/null @@ -1,110 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd63 - name: Previously Seen AWS Regions - type: splunk -confidence: medium -creation_date: '2018-02-01' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where an instance is started - in a particular region in the last one hour and then compares it to a lookup file - of previously seen regions where an instance was started -detect: - splunk: - correlation_rule: - notable: - nes_fields: awsRegion - rule_description: An AWS instance is started in a new, previously unseen, - region - rule_title: AWS instance is started in a new region - risk: - risk_object: awsRegion - risk_object_type: - - other - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail earliest=-1h StartInstances - | stats earliest(_time) as earliest latest(_time) as latest by awsRegion - | inputlookup append=t previously_seen_aws_regions.csv - | stats min(earliest) as earliest max(latest) as latest by awsRegion - | outputlookup previously_seen_aws_regions.csv - | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") - | `security_content_ctime(earliest)` - | `security_content_ctime(latest)` - | where regionStatus="Instance Started in a New Region" - suppress: - suppress_fields: awsRegion - suppress_period: 14400s -eli5: In this search, we query CloudTrail logs to look for events that indicate that - an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` - lookup file created using the support search, we compare the region where this instance - was started to all previously observed regions. The `eval` and `if` functions determine - that the earliest times seen for this region and instance were within the last day. - If a new region is detected, it will alert you with "Instance Started in a New Region". - However, this region will be added to the list of `previously_seen_aws_regions.csv`. - Please maintain `previously_seen_aws_regions.csv` -entities: - - awsRegion -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the "Previously seen AWS Regions" support search only once to create - of baseline of previously seen regions. -id: ada0f478-84a8-4641-a3f3-d82362d6fd75 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: It's possible that a user has unknowingly started an instance - in a new region. Please verify that this activity is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 12 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - nist: - - DE.DP - - DE.AE -modification_date: '2018-02-23' -name: EC2 Instance Started In Previously Unseen Region -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_city.yml b/detections/aws_cloud_provisioning_from_previously_unseen_city.yml index 5a41797557..a128e3fb3b 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_city.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_city.yml @@ -1,75 +1,28 @@ -asset_type: AWS Instance -baselines: - - id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee - name: Previously Seen AWS Provisioning Activity Sources - type: splunk -confidence: medium -creation_date: '2018-03-16' -data_metadata: - data_source: - - AWS CloudTrail - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: AWS Cloud Provisioning From Previously Unseen City +id: 344a1778-0b25-490c-adb1-de8beddf59cd +version: 1 +date: '2018-03-16' description: 'This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." ' -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, city - rule_description: Your AWS infrastructure was provisioned from a city, $city$, - which has never before been seen provisioning your infrastructure. - rule_title: AWS Provision Activity From $city$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation - sourceIPAddress | search City=* [search sourcetype=aws:cloudtrail (eventName=Run* - OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats - earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, - City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, - City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval - newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 - | table City] | spath output=user userIdentity.arn | rename sourceIPAddress - as src_ip | table _time, user, src_ip, City, eventName, errorCode - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: The subsearch returns all events with event names that start with "Run" or "Create," - and then does a `GeoIP` lookup on the IP address that initiated the action within - the last hour. It appends the historical data to those results in the lookup file. - Next, it recalculates the `firstTime` and `lastTime` field for each country, region, - city, and IP address and outputs this data to the lookup file to update the local - cache. It then calculates the `firstTime` and `lastTime` for each city. It returns - only those events from cities that have first been seen in the past hour. This is - combined with the main search to return the time, user, IP address, city, event - name, and error code from the action. -entities: - - dest how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -id: 344a1778-0b25-490c-adb1-de8beddf59cd -investigations: - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: 0abeeb40-1255-4b68-91d1-7a7eb410c4b8 - name: Get All AWS Activity From City - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress + | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation + sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) + as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv + | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, + City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv + | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime + >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath + output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, + src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter`' known_false_positives: "This is a strictly behavioral search, so we define \"false\ \ positive\" slightly differently. Every time this fires, it will accurately reflect\ \ the first occurrence in the time period you're searching within, plus what is\ @@ -81,23 +34,12 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal \ free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\ \ (particularly small countries in less economically powerful regions), this may\ \ be much less valuable to you." -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: +tags: + analytics_story: + - AWS Suspicious Provisioning Activities cis20: - - CIS 1 + - CIS 1 nist: - - ID.AM -modification_date: '2018-03-16' -name: AWS Cloud Provisioning From Previously Unseen City -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_country.yml b/detections/aws_cloud_provisioning_from_previously_unseen_country.yml index 6ce3c7218c..2aba2f4827 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_country.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_country.yml @@ -1,76 +1,29 @@ -asset_type: AWS Instance -baselines: - - id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee - name: Previously Seen AWS Provisioning Activity Sources - type: splunk -confidence: medium -creation_date: '2018-03-16' -data_metadata: - data_source: - - AWS CloudTrail - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: AWS Cloud Provisioning From Previously Unseen Country +id: ceb8d3d8-06cb-49eb-beaf-829526e33ff0 +version: 1 +date: '2018-03-16' description: 'This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." ' -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, country - rule_description: Your AWS infrastructure was provisioned from a country, - $country$, which has never before been seen provisioning your infrastructure. - rule_title: AWS Provision Activity From $country$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation - sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* - OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats - earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, - City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, - City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | - eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where - newCountry=1 | table Country] | spath output=user userIdentity.arn | rename - sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, - errorCode - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: The subsearch returns all events with event names that start with "Run" or "Create," - and then does a `GeoIP` lookup on the IP address that initiated the action within - the last hour. It appends the historical data to those results in the lookup file. - Next, it recalculates the `firstTime` and `lastTime` field for each country, region, - city, and IP address and outputs this data to the lookup file to update the local - cache. It then calculates the `firstTime` and `lastTime` for each country. It returns - only those events from countries that have first been seen in the past hour. This - is combined with the main search to return the time, user, IP address, city, event - name, and error code from the action. -entities: - - dest how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -id: ceb8d3d8-06cb-49eb-beaf-829526e33ff0 -investigations: - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: e763cdb9-00da-41e0-9bda-444debc9501a - name: Get All AWS Activity From Country - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress + | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | + iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, + latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup + append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as + firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | + outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime + >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | + spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, + user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter`' known_false_positives: "This is a strictly behavioral search, so we define \"false\ \ positive\" slightly differently. Every time this fires, it will accurately reflect\ \ the first occurrence in the time period you're searching over plus what is stored\ @@ -82,23 +35,12 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal \ version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\ \ (particularly small countries in less economically powerful regions), this may\ \ be much less valuable to you." -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: +tags: + analytics_story: + - AWS Suspicious Provisioning Activities cis20: - - CIS 1 + - CIS 1 nist: - - ID.AM -modification_date: '2018-03-16' -name: AWS Cloud Provisioning From Previously Unseen Country -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_ip.yml b/detections/aws_cloud_provisioning_from_previously_unseen_ip.yml deleted file mode 100644 index 6a0860dcfc..0000000000 --- a/detections/aws_cloud_provisioning_from_previously_unseen_ip.yml +++ /dev/null @@ -1,99 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee - name: Previously Seen AWS Provisioning Activity Sources - type: splunk -confidence: medium -creation_date: '2018-03-16' -data_metadata: - data_source: - - AWS CloudTrail - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: 'This search looks for AWS provisioning activities from previously unseen - IP addresses. Provisioning activities are defined broadly as any event that begins - with "Run" or "Create." ' -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip - rule_description: Your AWS infrastructure was provisioned from an IP, $src_ip$, - which has never before been seen provisioning your infrastructure. - rule_title: AWS Provision Activity From $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) [search sourcetype=aws:cloudtrail (eventName=Run* - OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats - earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, - City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, - City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress - | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where - newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename - sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode - suppress: - suppress_fields: src_ip - suppress_period: 14400s -eli5: The subsearch returns all events with event names that start with "Run" or "Create," - and then does a `GeoIP` lookup on the IP address that initiated the action within - the last hour. It appends the historical data to those results in the lookup file. - Next, it recalculates the `firstTime` and `lastTime` field for each country, region, - city, and IP address and outputs this data to the lookup file to update the local - cache. It then calculates the `firstTime` and `lastTime` for each city. It returns - only those events from IP addresses that have first been seen in the past hour. - This is combined with the main search to return the time, user, IP address, city, - event name, and error code from the action. -entities: - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. This search works best when you run the "Previously Seen AWS Provisioning - Activity Sources" support search once to create a history of previously seen locations - that have provisioned AWS resources. -id: 42e15012-ac14-4801-94f4-f1acbe64880b -investigations: - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk -known_false_positives: "This is a strictly behavioral search, so we define \"false\ - \ positive\" slightly differently. Every time this fires, it will accurately reflect\ - \ the first occurrence in the time period you're searching within, plus what is\ - \ stored in the cache feature. But while there are really no \"false positives\"\ - \ in a traditional sense, there is definitely lots of noise.\\\n This search will\ - \ fire any time a new IP address is seen in the **GeoIP** database for any kind\ - \ of provisioning activity. If you typically do all provisioning from tools inside\ - \ of your country, there should be few false positives. If you are located in countries\ - \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\ - \ has weak resolution (particularly small countries in less economically powerful\ - \ regions), this may be much less valuable to you." -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-03-16' -name: AWS Cloud Provisioning From Previously Unseen IP Address -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_ip_address.yml b/detections/aws_cloud_provisioning_from_previously_unseen_ip_address.yml new file mode 100644 index 0000000000..ad43331469 --- /dev/null +++ b/detections/aws_cloud_provisioning_from_previously_unseen_ip_address.yml @@ -0,0 +1,45 @@ +name: AWS Cloud Provisioning From Previously Unseen IP Address +id: 42e15012-ac14-4801-94f4-f1acbe64880b +version: 1 +date: '2018-03-16' +description: 'This search looks for AWS provisioning activities from previously unseen + IP addresses. Provisioning activities are defined broadly as any event that begins + with "Run" or "Create." ' +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. This search works best when you run the "Previously Seen AWS Provisioning + Activity Sources" support search once to create a history of previously seen locations + that have provisioned AWS resources. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* + OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) + as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country + | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country + | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime + >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] + | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table + _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter`' +known_false_positives: "This is a strictly behavioral search, so we define \"false\ + \ positive\" slightly differently. Every time this fires, it will accurately reflect\ + \ the first occurrence in the time period you're searching within, plus what is\ + \ stored in the cache feature. But while there are really no \"false positives\"\ + \ in a traditional sense, there is definitely lots of noise.\\\n This search will\ + \ fire any time a new IP address is seen in the **GeoIP** database for any kind\ + \ of provisioning activity. If you typically do all provisioning from tools inside\ + \ of your country, there should be few false positives. If you are located in countries\ + \ where the free version of **MaxMind GeoIP** that ships by default with Splunk\ + \ has weak resolution (particularly small countries in less economically powerful\ + \ regions), this may be much less valuable to you." +tags: + analytics_story: + - AWS Suspicious Provisioning Activities + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/aws_cloud_provisioning_from_previously_unseen_region.yml b/detections/aws_cloud_provisioning_from_previously_unseen_region.yml index 484ff855cb..4ae3c4aa77 100644 --- a/detections/aws_cloud_provisioning_from_previously_unseen_region.yml +++ b/detections/aws_cloud_provisioning_from_previously_unseen_region.yml @@ -1,76 +1,28 @@ -asset_type: AWS Instance -baselines: - - id: ac88e6a0-4fba-4dfd-b7b9-8964df7d1aee - name: Previously Seen AWS Provisioning Activity Sources - type: splunk -confidence: medium -creation_date: '2018-03-16' -data_metadata: - data_source: - - AWS CloudTrail - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: AWS Cloud Provisioning From Previously Unseen Region +id: 7971d3df-da82-4648-a6e5-b5637bea5253 +version: 1 +date: '2018-03-16' description: This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, Region - rule_description: Your AWS infrastructure was provisioned from a region, $Region$, - which has never before been seen provisioning your infrastructure. - rule_title: AWS Provision Activity From $region$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation - sourceIPAddress | search Region=* [search sourcetype=aws:cloudtrail (eventName=Run* - OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats - earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, - City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, - City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv - | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | - eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where - newRegion=1 | table Region] | spath output=user userIdentity.arn | rename - sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, - errorCode - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: The subsearch returns all events with event names that start with "Run" or "Create," - and then does a `GeoIP` lookup on the IP address that initiated the action within - the last hour. It appends the historical data to those results in the lookup file. - Next, it recalculates the `firstTime` and `lastTime` field for each country, region, - city, and IP address and outputs this data to the lookup file to update the local - cache. It then calculates the `firstTime` and `lastTime` for each city. It returns - only those events from regions that have first been seen in the past hour. This - is combined with the main search to return the time, user, IP address, city, event - name, and error code from the action. -entities: - - dest how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -id: 7971d3df-da82-4648-a6e5-b5637bea5253 -investigations: - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: 5b794bef-1743-4f6f-804a-43915a2702ff - name: Get All AWS Activity From Region - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress + | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation + sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) + as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv + | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, + City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv + | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime + >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath + output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, + src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter`' known_false_positives: "This is a strictly behavioral search, so we define \"false\ \ positive\" slightly differently. Every time this fires, it will accurately reflect\ \ the first occurrence in the time period you're searching within, plus what is\ @@ -82,23 +34,12 @@ known_false_positives: "This is a strictly behavioral search, so we define \"fal \ version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution\ \ (particularly small countries in less economically powerful regions), this may\ \ be much less valuable to you." -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: +tags: + analytics_story: + - AWS Suspicious Provisioning Activities cis20: - - CIS 1 + - CIS 1 nist: - - ID.AM -modification_date: '2018-03-16' -name: AWS Cloud Provisioning From Previously Unseen Region -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/aws_cross_account_activity_from_new_account.yml b/detections/aws_cross_account_activity_from_new_account.yml deleted file mode 100644 index ce1f8f645f..0000000000 --- a/detections/aws_cross_account_activity_from_new_account.yml +++ /dev/null @@ -1,128 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: 1cc22b09-c867-416e-a511-cb36ac44aee2 - name: Previously Seen AWS Cross Account Activity - type: splunk -confidence: medium -creation_date: '2018-02-01' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for AssumeRole events where an IAM role in a different - account is requested for the first time. -detect: - splunk: - correlation_rule: - notable: - nes_fields: requestingAccountId, requestedAccountId, src_user, dest_user - rule_description: Access to $dest_user$ was requested for the first time by - $src_user$ - rule_title: AWS Account $dest_user$ access by $src_user$ - risk: - risk_object: dest_user - risk_object_type: - - user - risk_score: 20 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId - path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId - | search requestingAccountId=* | where requestingAccountId != requestedAccountId - | inputlookup append=t previously_seen_aws_cross_account_activity | multireport - [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) - as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) - as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity - | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d - %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), - lastTime))) as lastTime by requestingAccountId, requestedAccountId | where - firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId - path=responseElements.credentials.accessKeyId | spath output=requestingARN - path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) - as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, - values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) - as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] - | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, - requestedAccountId, awsRegion, accessKeyId, sharedEventID - suppress: - suppress_fields: requestingAccountId, requestedAccountId - suppress_period: 14400s -eli5: 'This search\ - - 1. Retrieves the **AssumeRole** event\ - - 1. Verifies that the log entry contains a value for the account ID of the requesting - account\ - - 1. Ensures that the requesting account ID does not match the account ID of the requested - account\ - - 1. Pulls in the previously seen requesting and requested account IDs\ - - 1. Splits up and executes multiple search paths at the same.\ - - 1. The first path determines the **firstTime** and **lastTime** entries for the - cache file\ - - 1. Outputs the data to the cache file.\ - - 1. Creates a conditional statement that is always false (both because we don''t - want these values to exit the search pipeline and because we think we''re clever).The - second pipeline adds the **firstTime** and **lastTime** entries to search results. - Next, it filters out any account pairs that haven''t been seen for the first time - within the last hour. The `isnotnull(_time)` will remove the entries from the cache - file.\ - - The search finishes by gathering the data that it will display to the user.' -entities: - - dest_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the `Previously Seen AWS Cross Account Activity` support search only - once to create the baseline of previously seen cross account activity. Thanks to - Pablo Vega at Recurly for suggesting improvements to the search. -id: 64fbbddf-fabf-4edf-80b3-0cc36ef37727 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: 703b65a4-a0ae-4171-965d-45507506c64f - name: AWS Investigate User Activities By AccessKeyId - type: splunk - - id: b0d2e6a8-75fa-4b1b-9486-3d32acadf891 - name: AWS Investigate User Activities By Source User - type: splunk -known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior. - It's suspicious when an account requests privileges of an account it hasn't before. - You should validate with the account owner that this is a legitimate request. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - nist: - - PR.AC - - PR.DS - - DE.AE -modification_date: '2018-11-02' -name: AWS Cross Account Activity From Previously Unseen Account -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/aws_cross_account_activity_from_previously_unseen_account.yml b/detections/aws_cross_account_activity_from_previously_unseen_account.yml new file mode 100644 index 0000000000..0856164363 --- /dev/null +++ b/detections/aws_cross_account_activity_from_previously_unseen_account.yml @@ -0,0 +1,45 @@ +name: AWS Cross Account Activity From Previously Unseen Account +id: 64fbbddf-fabf-4edf-80b3-0cc36ef37727 +version: 3 +date: '2018-11-02' +description: This search looks for AssumeRole events where an IAM role in a different + account is requested for the first time. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the `Previously Seen AWS Cross Account Activity` support search only + once to create the baseline of previously seen cross account activity. Thanks to + Pablo Vega at Recurly for suggesting improvements to the search. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId + | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* + | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity + | multireport [| stats min(eval(coalesce(firstTime, _time))) + as firstTime max(eval(coalesce(lastTime, _time))) + as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity + | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, _time))) as firstTime, + max(eval(coalesce(lastTime, _time))) as lastTime by requestingAccountId, requestedAccountId | where firstTime + >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId + path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN + | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) + as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, + values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, + requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, + dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID | `aws_cross_account_activity_from_previously_unseen_account_filter`' +known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior. + It's suspicious when an account requests privileges of an account it hasn't before. + You should validate with the account owner that this is a legitimate request. +tags: + analytics_story: + - AWS Cross Account Activity + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - PR.AC + - PR.DS + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/aws_network_access_control_list_created_with_all_open_ports.yml b/detections/aws_network_access_control_list_created_with_all_open_ports.yml new file mode 100644 index 0000000000..8efc7ccc2b --- /dev/null +++ b/detections/aws_network_access_control_list_created_with_all_open_ports.yml @@ -0,0 +1,32 @@ +name: AWS Network Access Control List Created with All Open Ports +id: ada0f478-84a8-4641-a3f1-d82362d6bd75 +version: 1 +date: '2017-01-10' +description: The search looks for CloudTrail events to detect if any network ACLs + were created with all the ports open to a specified CIDR. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail + inputs. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=CreateNetworkAclEntry | mvexpand requestParameters + | mvexpand responseElements | search requestParameters.portRange.from=1024 requestParameters.portRange.to=65535 + requestParameters.ruleAction=allow | rename userIdentity.arn as arn | rename requestParameters.networkAclId + as networkAclId | table _time aws_account_id src userName arn networkAclId requestParameters.* + responseElements.* | `aws_network_access_control_list_created_with_all_open_ports_filter`' +known_false_positives: It's possible that an admin has created this ACL with all ports + open for some legitimate purpose however, this should be scoped and not allowed + in production environment. +tags: + analytics_story: + - AWS Network ACL Activity + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 11 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/aws_network_access_control_list_deleted.yml b/detections/aws_network_access_control_list_deleted.yml new file mode 100644 index 0000000000..a4afbde38b --- /dev/null +++ b/detections/aws_network_access_control_list_deleted.yml @@ -0,0 +1,33 @@ +name: AWS Network Access Control List Deleted +id: ada0f478-84a8-4641-a3f1-d82362d6fd75 +version: 1 +date: '2017-01-10' +description: Enforcing network-access controls is one of the defensive mechanisms + used by cloud administrators to restrict access to a cloud instance. After the attacker + has gained control of the AWS console by compromising an admin account, they can + delete a network ACL and gain access to the instance from anywhere. This search + will query the CloudTrail logs to detect users deleting network ACLs. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | + stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) + values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `aws_network_access_control_list_deleted_filter`' +known_false_positives: It's possible that a user has legitimately deleted a network + ACL. +tags: + analytics_story: + - AWS Network ACL Activity + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 11 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/aws_spike_acl_activity.yml b/detections/aws_spike_acl_activity.yml deleted file mode 100644 index 00446a4958..0000000000 --- a/detections/aws_spike_acl_activity.yml +++ /dev/null @@ -1,151 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edd96-ff2b-4810-9f1f-63da3783fd63 - name: Baseline of Network ACL Activity by ARN - type: splunk -confidence: medium -creation_date: '2018-05-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search will detect users creating spikes in API activity related - to network access-control lists (ACLs)in your AWS environment. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A spike in the number of AWS API calls related to network - ACLs by $user$ was detected. - rule_title: Spike in AWS Network ACL activity detected by $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 10 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `network_acl_events` [search sourcetype=aws:cloudtrail - `network_acl_events` | spath output=arn path=userIdentity.arn | stats count - as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | - fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount - | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, - 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval - avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, - stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) - | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup - network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold - = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn - as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn - | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) - as uniqueApisCalled by user - suppress: - suppress_fields: user - suppress_period: 14400s -eli5: 'This search and its corresponding subsearch run through the following series - of steps: \ - - 1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls - specifically for creating/modifying/replacing network Access Control Lists (ACLs).\ - - 1. Kick off a subsearch that retrieves the same data and pulls out the ARN into - a more friendly format.\ - - 1. Count the number of API calls per Amazon Resource Name (ARN).\ - - 1. Load the cache file that contains the number of data points, the count from the - latest hour, the API call average, and the standard deviation for each ARN.\ - - 1. Drop the count from the latest hour, since it is not necessary, and merge the - rest of the data with the results of the stats command. \ - - 1. Rename `apiCalls` as `latestCount`.\ - - 1. Calculate the new average value for each ARN with the latest count, weighting - the past much more heavily than the current hour. They do the same for the standard - deviation--weighting the past more heavily than the current.\ - - 1. Update the cache file with the latest results.\ - - 1. Set the minimum threshold for the number of data points and set the number of - standard deviations away from the mean it must be to be considered a spike.\ - - 1. Make a determination regarding whether or not the current count is a spike by - checking to see if the minimum data-point threshold has been met and the count is - a sufficient number of standard deviations away from the average.\ - - 1. Filter out anything that it determines is not a spike and return the list of - ARNs to the main search. The main search subsequently gets the names of all the - API calls, the number of unique API calls, and the total number of API calls for - each of these ARNs. Finally, it looks up the average and standard deviation and - returns both the average and the number of standard deviations the spike is from - the average.' -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the minimum number of data - points required to have a statistically significant amount of data to determine. - The `deviationThreshold` variable is the number of standard deviations away from - the mean that the value must be to be considered a spike. This search works best - when you run the "Baseline of Network ACL Activity by ARN" support search once to - create a lookup file of previously seen Network ACL Activity. To add or remove API - event names related to network ACLs, edit the macro `network_acl_events`. -id: ada0f478-84a8-4641-a1f1-e32372d4bd53 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241 - name: AWS Network Interface details via resourceId - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242 - name: AWS Network ACL Details from ID - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` - and `deviationThreshold`. Please modify this according the your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 12 - - CIS 11 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Exfiltration - nist: - - DE.DP - - DE.CM - - PR.AC -modification_date: '2018-05-21' -name: Detect Spike in Network ACL Activity -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/badrabbit_schtasks.yml b/detections/badrabbit_schtasks.yml deleted file mode 100644 index 84fa769397..0000000000 --- a/detections/badrabbit_schtasks.yml +++ /dev/null @@ -1,111 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-11-03' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for flags passed to schtasks.exe on the command-line - that indicate that task names related to the execution of Bad Rabbit ransomware - were created or deleted. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: This search looks for flags passed to schtasks.exe on the - command-line that indicate that task names specific to Bad Rabbit ransomware - has been created or deleted - rule_title: Scheduled tasks used in BadRabbit ransomware detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Processes.process) as process from datamodel=Endpoint.Processes - where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR - Processes.process= "*delete*") by Processes.parent_process Processes.process_name - Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` - | search (process=*rhaegal* OR process=*drogon* OR *viserion_*)' - suppress: - suppress_fields: dest, process_name - suppress_period: 28800s -eli5: The search looks for execution of schtasks.exe with parameters that indicate - that specific task names related to the Bad Rabbit ransomware were created or deleted. - The specific task name used are rhaegal, drogon and viserion_. Schtasks.exe is a - native windows program that is used to schedule tasks on local or remote systems. - Attackers often leverage this capability to schedule the execution of commands or - establish persistence. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: 1297fb80-f42a-4b4a-9c8b-78c066437cf6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: No known false positives -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Lateral Movement - - Execution - - Scheduled Task - nist: - - PR.IP -modification_date: '2019-02-28' -name: Scheduled tasks used in BadRabbit ransomware -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/batch_file_write_system32.yml b/detections/batch_file_write_system32.yml deleted file mode 100644 index 0c26711c0e..0000000000 --- a/detections/batch_file_write_system32.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-12-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: The search looks for a batch file (.bat) written to the Windows system - directory tree. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A batch file was written to the system directory on $dest$. - rule_title: Batch file write to system32 detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name - values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path - | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| - rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* - AND file_extension=.bat' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at file modifications across your hosts, as well as for evidence - of batch files being written to paths that include "system32." This activity is - consistent with some SamSam attacks and is, in general, suspicious. -entities: - - dest -how_to_implement: You must be ingesting data that records the file-system activity - from your hosts to populate the Endpoint file-system data-model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. -id: 503d17cb-9eab-4cf8-a20e-01d5c6987ae3 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc - name: Investigate Successful Remote Desktop Authentications - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: It is possible for this search to generate a notable event - for a batch file write to a path that includes the string "system32", but is not - the actual Windows system directory. As such, you should confirm the path of the - batch file identified by the search. In addition, a false positive may be generated - by an administrator copying a legitimate batch file in this directory tree. You - should confirm that the activity is legitimate and modify the search to add exclusions, - as necessary. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Delivery - mitre_attack: [] - nist: - - PR.PT - - DE.CM -modification_date: '2018-12-14' -name: Batch File Write to System32 -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/batch_file_write_to_system32.yml b/detections/batch_file_write_to_system32.yml new file mode 100644 index 0000000000..b654a689e7 --- /dev/null +++ b/detections/batch_file_write_to_system32.yml @@ -0,0 +1,38 @@ +name: Batch File Write to System32 +id: 503d17cb-9eab-4cf8-a20e-01d5c6987ae3 +version: 1 +date: '2018-12-14' +description: The search looks for a batch file (.bat) written to the Windows system + directory tree. +how_to_implement: You must be ingesting data that records the file-system activity + from your hosts to populate the Endpoint file-system data-model node. If you are + using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which + you want to collect data. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name + values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path + | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| + rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* + AND file_extension=.bat | `batch_file_write_to_system32_filter`' +known_false_positives: It is possible for this search to generate a notable event + for a batch file write to a path that includes the string "system32", but is not + the actual Windows system directory. As such, you should confirm the path of the + batch file identified by the search. In addition, a false positive may be generated + by an administrator copying a legitimate batch file in this directory tree. You + should confirm that the activity is legitimate and modify the search to add exclusions, + as necessary. +tags: + analytics_story: + - SamSam Ransomware + kill_chain_phases: + - Delivery + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/brand_abuse_dns.yml b/detections/brand_abuse_dns.yml deleted file mode 100644 index 5fdebb30ba..0000000000 --- a/detections/brand_abuse_dns.yml +++ /dev/null @@ -1,98 +0,0 @@ -asset_type: Endpoint -baselines: - - id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f - name: DNSTwist Domain Names - type: splunk -confidence: high -creation_date: '2017-06-01' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search looks for DNS requests for faux domains similar to the domains - that you want to have monitored for abuse. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, query - rule_description: The host $src$ issued a DNS request for a domain to that - which you are monitoring for brand abuse. - rule_title: DNS Query Brand Abuse from $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime - from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)`| `brand_abuse_dns`' - suppress: - suppress_fields: src,query - suppress_period: 14400s -eli5: This search gathers all the answers to each system's DNS query, then filters - out all queries that do not appear on the list of faux "look-a-like" domains that - have been generated from the brand abuse domains you are monitoring. -entities: - - src -how_to_implement: You need to ingest data from your DNS logs. Specifically you must - ingest the domain that is being queried and the IP of the host originating the request. - Ideally, you should also be ingesting the answer to the query and the query type. - This approach allows you to also create your own localized passive DNS capability - which can aid you in future investigations. You also need to have run the search - "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that - will be checked for. -id: 24dd17b1-e2fb-4c31-878c-d4f746595bfa -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None at this time -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - kill_chain_phases: - - Delivery - - Actions on Objectives -modification_date: '2017-09-23' -name: Monitor DNS For Brand Abuse -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/brand_abuse_email.yml b/detections/brand_abuse_email.yml deleted file mode 100644 index 2471d880ac..0000000000 --- a/detections/brand_abuse_email.yml +++ /dev/null @@ -1,106 +0,0 @@ -asset_type: Endpoint -baselines: - - id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f - name: DNSTwist Domain Names - type: splunk -confidence: high -creation_date: '2017-06-01' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange - - Bro - - Splunk Stream -description: This search looks for emails claiming to be sent from a domain similar - to one that you want to have monitored for abuse. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_user, message_id - rule_description: The sender $src_user$ has sent an email from a similar domain - to that which you are monitoring for brand abuse. - rule_title: Possible Brand Abuse from $src_user$ - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, - min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, - All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, - 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT - domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, - recipients, firstTime, lastTime' - suppress: - suppress_fields: message_id, src_user - suppress_period: 86400s -eli5: This search looks at the sender address in email headers, and identifies those - with a sender address using a domain name that matches the list of permutations - generated for the domain you want to monitor. -entities: - - src_user -how_to_implement: You need to ingest email header data. Specifically the sender's - address (src_user) must be populated. You also need to have run the search "ESCU - - DNSTwist Domain Names", which creates the permutations of the domain that will - be checked for. -id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd75 - name: Get Email Info - type: splunk - - id: 5df39b3f-447d-4869-b673-8f45ad4616fe - name: Get Emails From Specific Sender - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None at this time -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Delivery - nist: - - PR.IP -modification_date: '2018-01-05' -name: Monitor Email For Brand Abuse -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/brand_abuse_web.yml b/detections/brand_abuse_web.yml deleted file mode 100644 index cdc5a02cd2..0000000000 --- a/detections/brand_abuse_web.yml +++ /dev/null @@ -1,97 +0,0 @@ -asset_type: Endpoint -baselines: - - id: 19f7d2ec-6028-4d01-bcdb-bda9a034c17f - name: DNSTwist Domain Names - type: splunk -confidence: high -creation_date: '2017-06-01' -data_metadata: - data_models: - - Web - data_source: - - Web Traffic - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: This search looks for Web requests to faux domains similar to the one - that you want to have monitored for abuse. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, url - rule_description: The host $src$ connected to a web site with a domain similar - to that which you are monitoring for brand abuse. - rule_title: Web URL Brand Abuse from $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime - from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `brand_abuse_web`' - suppress: - suppress_fields: src - suppress_period: 86400s -eli5: This search looks at all the URLs an endpoint is connecting to and then checks - the URL against a list of faux domains that could be indicative of brand abuse. -entities: - - src -how_to_implement: You need to ingest data from your web traffic. This can be accomplished - by indexing data from a web proxy, or using a network traffic analysis tool, such - as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain - Names", which creates the permutations of the domain that will be checked for. -id: 134da869-e264-4a8f-8d7e-fcd0ec88f301 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None at this time -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Delivery - mitre_attack: [] - nist: - - PR.IP -modification_date: '2017-09-23' -name: Monitor Web Traffic For Brand Abuse -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/change_file_association.yml b/detections/change_file_association.yml deleted file mode 100644 index 79c4e289d8..0000000000 --- a/detections/change_file_association.yml +++ /dev/null @@ -1,121 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-01-26' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for changes to registry values that control Windows - file associations, executed by a process that is not typical for legitimate, routine - changes to this area. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name, process - rule_description: The system $dest$ had an unusual change to a file association - rule_title: Suspicious File Association Change on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - macros: - - change_file_association_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) - as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe - AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path - count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* - by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | - table process_id dest registry_path]| `change_file_association_filter`' - suppress: - suppress_fields: dest,user - suppress_period: 28800s -eli5: This search looks for changes made to the registry that control Windows file - associations. It is typical for users to change the file association to open certain - types of files with specific applications. However, when these changes are legitimately - performed, they are typically done via the processes explorer.exe or openwith.exe. - The search first executes the subsearch that looks at the Registry node, which specifies - setting a value in the registry and creates a table of process_id and dest. It then - uses those arguments to find out what process and parent process were responsible - for making those registry changes. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting information - on registry changes that include the name of the process responsible for the changes - from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` - nodes. -id: 1b989a0e-0129-4446-a695-f193a5b746fc -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There may be other processes in your environment that users - may legitimately use to modify file associations. If this is the case and you are - finding false positives, you can modify the search to add those processes as exceptions. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Change Default File Association - nist: - - DE.CM - - PR.PT - - PR.IP -modification_date: '2020-03-16' -name: Suspicious Changes to File Associations -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/child_processes_of_spoolsv_exe.yml b/detections/child_processes_of_spoolsv_exe.yml new file mode 100644 index 0000000000..5e1cb995bf --- /dev/null +++ b/detections/child_processes_of_spoolsv_exe.yml @@ -0,0 +1,41 @@ +name: Child Processes of Spoolsv exe +id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df +version: 3 +date: '2020-03-16' +description: This search looks for child processes of spoolsv.exe. This activity is + associated with a POC privilege-escalation exploit associated with CVE-2018-8440. + Spoolsv.exe is the process associated with the Print Spooler service in Windows + and typically runs as SYSTEM. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro + to filter out legitimate child processes spawned by spoolsv.exe. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process_name) + as process_name values(Processes.process) as process min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe + AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process + Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` ' +known_false_positives: Some legitimate printer-related processes may show up as children + of spoolsv.exe. You should confirm that any activity as legitimate and may be added + as exclusions in the search. +tags: + analytics_story: + - Windows Privilege Escalation + mitre_attack_id: + - T1068 + kill_chain_phases: + - Exploitation + cis20: + - CIS 5 + - CIS 8 + nist: + - PR.AC + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/children_of_spoolsv.yml b/detections/children_of_spoolsv.yml deleted file mode 100644 index 71eaef8c88..0000000000 --- a/detections/children_of_spoolsv.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-11-26' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for child processes of spoolsv.exe. This activity is - associated with a POC privilege-escalation exploit associated with CVE-2018-8440. - Spoolsv.exe is the process associated with the Print Spooler service in Windows - and typically runs as SYSTEM. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, parent_process_name - rule_description: A child process of spoolsv.exe was detected on $dest$. - rule_title: Spoolsv.exe spawned a child process on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - macros: - - children_of_spoolsv_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name - values(Processes.process) as process min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe - AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process - Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `children_of_spoolsv_filter`' - suppress: - suppress_fields: dest, parent_process_name - suppress_period: 86400s -eli5: This search looks for child processes of spoolsv.exe, which is associated with - the Print Spooler service on Windows. Children of this process typically run under - the SYSTEM context. This search should address the POC developed for the Windows - local-privilege-escalation exploit announced in September of 2018. The associated - vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. -id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Some legitimate printer-related processes may show up as children - of spoolsv.exe. You should confirm that any activity as legitimate and may be added - as exclusions in the search. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 5 - - CIS 8 - kill_chain_phases: - - Exploitation - mitre_attack: - - Privilege Escalation - - Exploitation for Privilege Escalation - nist: - - PR.AC - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Child Processes of Spoolsv.exe -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/clients_connecting_to_multiple_dns_servers.yml b/detections/clients_connecting_to_multiple_dns_servers.yml index bdcaaf78af..b3f12496ec 100644 --- a/detections/clients_connecting_to_multiple_dns_servers.yml +++ b/detections/clients_connecting_to_multiple_dns_servers.yml @@ -1,51 +1,9 @@ -asset_type: Endpoint -baselines: [] -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: Clients Connecting to Multiple DNS Servers +id: 74ec6f18-604b-4202-a567-86b2066be3ce +version: 2 +date: '2020-01-16' description: This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. -detect: - splunk: - correlation_rule: - macros: - - clients_connecting_to_multiple_dns_servers_output_filter - notable: - nes_fields: src, dest - rule_description: This search allows you to identify the endpoints that have - connected to more than five DNS servers over the time frame specified in - the search. - rule_title: Client $src$ Connecting to Multiple DNS Servers - risk: - risk_object: src - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) - as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY - by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count - > 5 | `clients_connecting_to_multiple_dns_servers_output_filter`' - suppress: - suppress_fields: src - suppress_period: 86400s -eli5: DNS Queries with multiple DNS servers from a single client is unusual and may - be indicative of malicious activity. This search works by performing a count by - the source of the distinct destinations for the DNS traffic. The search uses the - `Network_Resolution` data model. -entities: - - dest - - src how_to_implement: 'This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ @@ -59,40 +17,31 @@ how_to_implement: 'This search requires that DNS data is being ingested and popu Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: 74ec6f18-604b-4202-a567-86b2066be3ce -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk +type: ESCU +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count, values(DNS.dest) AS dest + dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY + by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | + `clients_connecting_to_multiple_dns_servers_filter` ' known_false_positives: It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 9 - - CIS 12 - - CIS 13 +tags: + analytics_story: + - DNS Hijacking + - Command and Control + - Suspicious DNS Traffic + - Host Redirection + mitre_attack_id: + - T1048 kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Exfiltration - - Exfiltration Over Alternative Protocol + - Command and Control + cis20: + - CIS 9 + - CIS 12 + - CIS 13 nist: - - PR.PT - - DE.AE - - PR.DS -modification_date: '2020-01-16' -name: Clients Connecting to Multiple DNS Servers -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' + - PR.PT + - DE.AE + - PR.DS + security_domain: network + asset_type: Endpoint diff --git a/detections/cloud_compute_activity_in_new_region.yml b/detections/cloud_compute_activity_in_new_region.yml deleted file mode 100644 index 39fc24bfa2..0000000000 --- a/detections/cloud_compute_activity_in_new_region.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: Cloud Compute Instance -baselines: - - id: b5e232db-dec6-4db8-aaa1-dd5474521e40 - name: Previously Seen Cloud Regions - type: splunk -confidence: medium -creation_date: '2019-10-02' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search looks at cloud-infrastructure events where an instance is - created in any region within the last hour and then compares it to a lookup file - of previously seen regions where instances have been created. -detect: - splunk: - correlation_rule: - lookups: - - previously_seen_cloud_regions - macros: - - previously_seen_cloud_regions_input_filter - - previously_seen_cloud_regions_search_window_begin_offset - notable: - drilldown_name: See all activity in $region$ - drilldown_search: '| from datamodel:Cloud_Infrastructure.Compute | search region="$region$"' - nes_fields: dest,region - rule_description: A cloud compute instance, $dest$, is started in a new, previously - unseen, region $region$ - rule_title: Cloud instance $dest$ started in a new region $region$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) - as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances - `previously_seen_cloud_regions_input_filter` by Compute.region, Compute.src_user - | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_regions - | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) - as dest by region, src_user | multireport [| table region, firstTime, lastTime - | outputlookup previously_seen_cloud_regions | where fact=fiction][| eval - new_region=if(firstTime >= relative_time(now(), `previously_seen_cloud_regions_search_window_begin_offset`), 1, 0) - | where new_region=1 - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table region, dest, src_user, - firstTime, lastTime' - suppress: - suppress_fields: dest,region - suppress_period: 14400s -eli5: In this search, we query cloud infrastructure compute logs to look for events - that indicate that an instance was started in a particular region. Using the \"previously_seen_cloud_regions\" - lookup file created using the support search, we compare the region where this instance - was started to all previously observed regions. The \"eval\" and \"if\" functions determine - that the earliest times seen for this region and instance were within the last day. - If a new region is detected, it will alert you with \"Instance Started in a New Region.\" - However, this region will be added to the list in \"previously_seen_cloud_regions.\" -entities: - - dest - - region - - src_user -how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs - and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) - installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously - seen regions. -id: fa4089e2-50e3-40f7-8469-d2cc1564ca59 -investigations: - - id: 3dc3a8e7-394b-44ae-8262-4ef8e90b723d - name: Investigate User Activities In Single Cloud Region - type: splunk - - id: 33a95cf2-900c-4636-8fca-5c5f71474720 - name: Investigate Cloud Compute Instance Activities - type: splunk -known_false_positives: It's possible that a user has unknowingly started an instance - in a new region. Please verify that this activity is legitimate. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 12 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - nist: - - DE.DP - - DE.AE -modification_date: '2019-10-02' -name: Cloud Compute Instance Started In Previously Unused Region -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/cloud_compute_instance_created_by_previously_unseen_user.yml b/detections/cloud_compute_instance_created_by_previously_unseen_user.yml index 4b6d0a0ffb..07dbaa29f6 100644 --- a/detections/cloud_compute_instance_created_by_previously_unseen_user.yml +++ b/detections/cloud_compute_instance_created_by_previously_unseen_user.yml @@ -1,97 +1,34 @@ -asset_type: Cloud Compute Instance -baselines: - - id: 9fa1c205-4e08-4681-bb1b-d0943e734b85 - name: Previously Seen Cloud Compute Creations By User - type: splunk -confidence: medium -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search looks for cloud compute instances created by users - who have not created them before. -detect: - splunk: - correlation_rule: - lookups: - - previously_seen_cloud_compute_creations_by_user - macros: - - previously_seen_cloud_compute_creations_by_user_input_filter - - previously_seen_cloud_compute_creations_by_user_search_window_begin_offset - notable: - drilldown_name: Show all instances created by $src_user$ - drilldown_search: '| from datamodel:Cloud_Infrastructure.Compute | action=run src_user=$src_user$' - nes_fields: src_user, dest - rule_description: The compute instance $dest$ was created by $src_user$. This - is the first time $src_user$ has created a compute instance. - rule_title: Cloud Compute Instance Created By Previously Unseen User $src_user$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) - as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run by - Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t - previously_seen_cloud_compute_creations_by_user | stats min(firstTime) as - firstTime max(lastTime) as lastTime, values(dest) as dest by src_user | multireport - [| table src_user, firstTime, lastTime | outputlookup previously_seen_cloud_compute_creations_by_user - | where fact=fiction][| eval new_user=if(firstTime >= relative_time(now(), - `previously_seen_cloud_compute_creations_by_user_search_window_begin_offset`), 1, 0) - | where new_user=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] - | table src_user, dest, firstTime, lastTime' - suppress: - suppress_fields: src_user, dest - suppress_period: 14400s -eli5: For each user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The - search then splits and outputs the updated times for each user back to the lookup - file and then clears out any output. The other part of the search limits the results - to when the user was seen for the first time within the previous 70 minutes. - It then displays the new user, the instances created by that user, and the associated times. -entities: - - dest - - src_user +name: Cloud Compute Instance Created By Previously Unseen User +id: 76988f6a-3935-48f6-a9e5-6fca8b3ed843 +version: 1 +date: '2018-03-12' +description: This search looks for cloud compute instances created by users who have + not created them before. how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) - installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of - baseline of previously seen users. -id: 76988f6a-3935-48f6-a9e5-6fca8b3ed843 -investigations: - - id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a - name: Investigate User Activities In All Cloud Regions - type: splunk - - id: 33a95cf2-900c-4636-8fca-5c5f71474720 - name: Investigate Cloud Compute Instance Activities - type: splunk -known_false_positives: It's possible that a user will start to create compute instances - for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-03-12' -name: Cloud Compute Instance Created By Previously Unseen User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey + installed. Run the "Previously Seen Cloud Compute Creations By User" support search + to create of baseline of previously seen users. +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` earliest(_time) as firstTime, latest(_time) + as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute + where Compute.action=run by Compute.src_user | `drop_dm_object_name("Compute")` + | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats + min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by src_user + | multireport [| table src_user, firstTime, lastTime | outputlookup previously_seen_cloud_compute_creations_by_user + | where fact=fiction][| eval new_user=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_creations_by_user_search_window_begin_offset`), + 1, 0) | where new_user=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] + | table src_user, dest, firstTime, lastTime | `cloud_compute_instance_created_by_previously_unseen_user_filter`' +known_false_positives: It's possible that a user will start to create compute instances + for the first time, for any number of reasons. Verify with the user launching instances + that this is the intended behavior. +tags: + analytics_story: + - Cloud Cryptomining + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: Cloud Compute Instance diff --git a/detections/cloud_compute_instance_created_with_previously_unseen_image.yml b/detections/cloud_compute_instance_created_with_previously_unseen_image.yml new file mode 100644 index 0000000000..11dfaa1d7d --- /dev/null +++ b/detections/cloud_compute_instance_created_with_previously_unseen_image.yml @@ -0,0 +1,34 @@ +name: Cloud Compute Instance Created With Previously Unseen Image +id: bc24922d-987c-4645-b288-f8c73ec194c4 +version: 1 +date: '2018-10-12' +description: This search looks for cloud compute instances being created with previously + unseen image IDs. +how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs + and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) + installed. Run the "Previously Seen Cloud Compute Images" support search to create + a baseline of previously seen images. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) + as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `cloud_compute_instance_created_with_previously_unseen_image_filter` + by Compute.image_id, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup + append=t previously_seen_cloud_compute_images | stats min(firstTime) as firstTime + max(lastTime) as lastTime, values(dest) as dest by image_id, src_user | multireport + [| table image_id, firstTime, lastTime | outputlookup previously_seen_cloud_compute_images + | where fact=fiction][| eval new_image=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_image_search_window_begin_offset`), + 1, 0) | where new_image=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] + | table image_id, dest, src_user, firstTime, lastTime' +known_false_positives: After a new image is created, the first systems created with + that image will cause this alert to fire. Verify that the image being used was + created by a legitimate user. +tags: + analytics_story: + - Cloud Cryptomining + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: Cloud Compute Instance diff --git a/detections/cloud_compute_instance_created_with_previously_unseen_image_id.yml b/detections/cloud_compute_instance_created_with_previously_unseen_image_id.yml deleted file mode 100644 index c6741eaaff..0000000000 --- a/detections/cloud_compute_instance_created_with_previously_unseen_image_id.yml +++ /dev/null @@ -1,96 +0,0 @@ -asset_type: Cloud Compute Instance -baselines: - - id: 3782ad10-5ce2-46e2-b9c4-1de9ecd3aecc - name: Previously Seen Cloud Compute Images - type: splunk -confidence: medium -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search looks for cloud compute instances being created with previously - unseen image IDs. -detect: - splunk: - correlation_rule: - lookups: - - previously_seen_cloud_compute_images - macros: - - previously_seen_cloud_compute_image_input_filter - - previously_seen_cloud_compute_image_search_window_begin_offset - notable: - drilldown_name: Show all instances created with image $image_id$ - drilldown_search: '| from datamodel:Cloud_Infrastructure.Compute | image_id=$image_id$' - nes_fields: dest - rule_description: The cloud compute instance $dest$ was created with previously - unused image $image_id$. - rule_title: Cloud Compute Instances Created With New Image - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) - as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` - by Compute.image_id, Compute.src_user | `drop_dm_object_name("Compute")` | - inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTime) - as firstTime max(lastTime) as lastTime, values(dest) as dest by image_id, - src_user | multireport [| table image_id, firstTime, lastTime | outputlookup - previously_seen_cloud_compute_images | where fact=fiction][| eval new_image=if(firstTime - >= relative_time(now(), `previously_seen_cloud_compute_image_search_window_begin_offset`), 1, 0) | where new_image=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table image_id, dest, src_user, firstTime, lastTime' - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: For each image ID and user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the - data. The search then splits and outputs the updated times for each image back to - the lookup file and clears out any output. The other part of the search limits - the results to when the image was seen for the first time within the previous 70 minutes. It then displays the new image, the instances created using it, the - user who created it, and the associated times. -entities: - - dest - - src_user -how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs - and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) - installed. Run the "Previously Seen Cloud Compute Images" support search to create - a baseline of previously seen images. -id: bc24922d-987c-4645-b288-f8c73ec194c4 -investigations: - - id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a - name: Investigate User Activities In All Cloud Regions - type: splunk - - id: 33a95cf2-900c-4636-8fca-5c5f71474720 - name: Investigate Cloud Compute Instance Activities - type: splunk -known_false_positives: After a new image is created, the first systems created with - that image will cause this alert to fire. Verify that the image being used was - created by a legitimate user. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-10-12' -name: Cloud Compute Instance Created With Previously Unseen Image -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/cloud_compute_instance_created_with_previously_unseen_instance_type.yml b/detections/cloud_compute_instance_created_with_previously_unseen_instance_type.yml index 2685bfc54b..e81483bfff 100644 --- a/detections/cloud_compute_instance_created_with_previously_unseen_instance_type.yml +++ b/detections/cloud_compute_instance_created_with_previously_unseen_instance_type.yml @@ -1,100 +1,34 @@ -asset_type: Cloud Compute Instance -baselines: - - id: 0ef13d46-164e-4cf5-816e-b3c0df170d00 - name: Previously Seen Cloud Compute Instance Types - type: splunk -confidence: medium -creation_date: '2019-10-03' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: Find EC2 instances being created with previously unseen - instance types. -detect: - splunk: - correlation_rule: - lookups: - - previously_seen_cloud_compute_instance_types - macros: - - previously_seen_cloud_compute_instance_types_input_filter - - previously_seen_cloud_compute_instance_types_search_window_begin_offset - notable: - drilldown_name: Find all compute instances of type $instance_type$ - drilldown_search: '| from datamodel:Cloud_Infrastructure.Compute | instance_type=$instance_type$' - nes_fields: instance_type - rule_description: The instance type $instance_type$ was used for the first - time to create $dest$. - rule_title: New Cloud Compute Instance Type $instance_type$ detected - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) - as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances - `previously_seen_cloud_compute_instance_types_input_filter` by Compute.instance_type, - Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t - previously_seen_cloud_compute_instance_types | stats min(firstTime) as firstTime - max(lastTime) as lastTime, values(dest) as dest by instance_type, src_user - | multireport [| table instance_type, firstTime, lastTime | outputlookup previously_seen_cloud_compute_instance_types - | where fact=fiction][| eval new_type=if(firstTime >= relative_time(now(), - `previously_seen_cloud_compute_instance_types_search_window_begin_offset`), 1, 0) - | where new_type=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] - | table instance_type, dest, src_user, firstTime, lastTime' - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: For each instance type and user, the search returns the first time seen, last time seen, and the system. It then appends the historical data and merges it into - the data. The search then splits and outputs the updated times for each instance - type back to the lookup file and clears out any output. The other part of the - search limits the results to when the instance type was seen for the first time - within the previous 70 minutes. It then displays the new instance type, the - instances created using it, the user who created them, and the times associated. -entities: - - dest - - src_user +name: Cloud Compute Instance Created With Previously Unseen Instance Type +id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda +version: 1 +date: '2018-03-12' +description: Find EC2 instances being created with previously unseen instance types. how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. -id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda -investigations: - - id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a - name: Investigate User Activities In All Cloud Regions - type: splunk - - id: 33a95cf2-900c-4636-8fca-5c5f71474720 - name: Investigate Cloud Compute Instance Activities - type: splunk -known_false_positives: It is possible that an admin will create a new system using - a new instance type that has never been used before. Verify with the creator that they intended - to create the system with the new instance type. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-03-12' -name: Cloud Compute Instance Created With Previously Unseen Instance Type -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) + as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances + `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` by + Compute.instance_type, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup + append=t previously_seen_cloud_compute_instance_types | stats min(firstTime) as + firstTime max(lastTime) as lastTime, values(dest) as dest by instance_type, src_user + | multireport [| table instance_type, firstTime, lastTime | outputlookup previously_seen_cloud_compute_instance_types + | where fact=fiction][| eval new_type=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_instance_types_search_window_begin_offset`), + 1, 0) | where new_type=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] + | table instance_type, dest, src_user, firstTime, lastTime' +known_false_positives: It is possible that an admin will create a new system using + a new instance type that has never been used before. Verify with the creator that + they intended to create the system with the new instance type. +tags: + analytics_story: + - Cloud Cryptomining + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: Cloud Compute Instance diff --git a/detections/cloud_compute_instance_started_in_previously_unused_region.yml b/detections/cloud_compute_instance_started_in_previously_unused_region.yml new file mode 100644 index 0000000000..f1e972da8a --- /dev/null +++ b/detections/cloud_compute_instance_started_in_previously_unused_region.yml @@ -0,0 +1,38 @@ +name: Cloud Compute Instance Started In Previously Unused Region +id: fa4089e2-50e3-40f7-8469-d2cc1564ca59 +version: 1 +date: '2019-10-02' +description: This search looks at cloud-infrastructure events where an instance is + created in any region within the last hour and then compares it to a lookup file + of previously seen regions where instances have been created. +how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs + and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) + installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search + to create a baseline of previously seen regions. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) + as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances + `cloud_compute_instance_started_in_previously_unused_region_filter` by Compute.region, + Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_regions + | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest + by region, src_user | multireport [| table region, firstTime, lastTime | outputlookup + previously_seen_cloud_regions | where fact=fiction][| eval new_region=if(firstTime + >= relative_time(now(), `previously_seen_cloud_regions_search_window_begin_offset`), + 1, 0) | where new_region=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] + | table region, dest, src_user, firstTime, lastTime' +known_false_positives: It's possible that a user has unknowingly started an instance + in a new region. Please verify that this activity is legitimate. +tags: + analytics_story: + - Cloud Cryptomining + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 12 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: Cloud Compute Instance diff --git a/detections/common_ransomware_extensions.yml b/detections/common_ransomware_extensions.yml index f276171835..b7ea897b2f 100644 --- a/detections/common_ransomware_extensions.yml +++ b/detections/common_ransomware_extensions.yml @@ -1,49 +1,9 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-08-21' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon +name: Common Ransomware Extensions +id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec +version: 3 +date: '2020-03-16' description: The search looks for file modifications with extensions commonly used by Ransomware -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file modification was detected on $dest$ with an extension - commonly used by ransomware. - rule_title: Ransomware Extension detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - macros: - - common_ransomware_extensions_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) - as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | - `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| - rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter`' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at file modifications across your hosts and identifies files - with extensions that are commonly associated with the encrypted files generated - by ransomware. -entities: - - dest how_to_implement: 'You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which @@ -62,62 +22,27 @@ how_to_implement: 'You must be ingesting data that records the filesystem activi Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: d98675ed-da43-4a7e-96a7-eeca3232ba8e - name: Get Update Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d44 - name: Get Backup Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) + as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex + field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter`' known_false_positives: It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 +tags: + analytics_story: + - SamSam Ransomware + - Ransomware kill_chain_phases: - - Actions on Objectives - mitre_attack: [] + - Actions on Objectives + cis20: + - CIS 8 nist: - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Common Ransomware Extensions -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/common_ransomware_notes.yml b/detections/common_ransomware_notes.yml index 6f21591ee3..4b8b8c9c48 100644 --- a/detections/common_ransomware_notes.yml +++ b/detections/common_ransomware_notes.yml @@ -1,111 +1,34 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-08-21' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon +name: Common Ransomware Notes +id: ada0f478-84a8-4641-a3f1-d82362d6bd71 +version: 3 +date: '2020-03-16' description: The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file modification associated with a ransomware victim - notification file detected on $dest$ - rule_title: Ransomware Note File detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - macros: - - common_ransomware_notes_filter - - ransomware_notes - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) - as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | - `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`|`ransomware_notes`| `common_ransomware_notes_filter`' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at file modifications in the Change Analysis data model. It - checks modified file names against an included lookup file, which contains the names - of note files left behind by ransomware (to inform the victim how they can pay the - ransom and retrieve their files). The search returns a list of files with matching - names. -entities: - - dest how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -id: ada0f478-84a8-4641-a3f1-d82362d6bd71 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: d98675ed-da43-4a7e-96a7-eeca3232ba8e - name: Get Update Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d44 - name: Get Backup Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) + as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | + `common_ransomware_notes_filter`' known_false_positives: It's possible that a legitimate file could be created with the same name used by ransomware note files. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 +tags: + analytics_story: + - SamSam Ransomware + - Ransomware kill_chain_phases: - - Actions on Objectives - mitre_attack: [] + - Actions on Objectives + cis20: + - CIS 8 nist: - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Common Ransomware Notes -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/container_implant_aws_detection.yml b/detections/container_implant_aws_detection.yml deleted file mode 100644 index d40a30cd62..0000000000 --- a/detections/container_implant_aws_detection.yml +++ /dev/null @@ -1,65 +0,0 @@ -asset_type: AWS ECR container -confidence: medium -creation_date: '2020-02-20' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - AWS Cloud Trail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This searches show information on uploaded containers including source user, image id, source IP - user type, http user agent, region, first time, last time of operation (PutImage). These searches - are based on Cloud Infrastructure Data Model. -detect: - splunk: - correlation_rule: - notable: - nes_fields: image_id, src_user, region, user_type - rule_description: A new container with image id $image_id$ uploaded to AWS ECR by user $src_user$ - rule_title: New container uploaded to AWS ECR - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 30 - macros: - - container_implant_aws_detection_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `container_implant_aws_detection_filter`' -eli5: In this search we can detect if a new container has been uploaded to Amazon Elastic Container Registry, operator can monitor users uploading containers, image ids of new uploaded containers. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for - AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. -id: f0f70b40-f7ad-489d-9905-23d149da8099 -known_false_positives: Uploading container is a normal behavior from developers or users with access to container registry. -investigations: - - id: 6027623f-7d10-4847-af3b-8d7e87970451 - name: Investigate AWS ECR container listing activity - type: splunk -maintainers: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -mappings: - mitre_attack: - - Persistence - mitre_technique_id: - - T1525 -modification_date: '2020-02-20' -name: New container uploaded to AWS ECR -original_authors: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/container_implant_gcp_detection.yml b/detections/container_implant_gcp_detection.yml deleted file mode 100644 index 3e2a472cc8..0000000000 --- a/detections/container_implant_gcp_detection.yml +++ /dev/null @@ -1,57 +0,0 @@ -asset_type: GCP GCR Container -confidence: medium -creation_date: '2020-02-20' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - google:gcp:pubsub:messages - providing_technologies: - - GCP -description: This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_user, account, bucket_name, event_name - rule_description: An GCP GCR container uploaded by $src_user$ - rule_title: GCP GCR container uploaded - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 30 - macros: - - container_implant_gcp_detection_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `container_implant_gcp_detection_filter`' -eli5: In this search we can detect if a new container has been uploaded to Google Container Registry, - operator can monitor users uploading containers, object paths of new uploaded containers. -entities: - - src_user -how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. -id: 4f00ca88-e766-4605-ac65-ae51c9fd185b -known_false_positives: Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. -maintainers: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -mappings: - mitre_attack: - - Persistence - mitre_technique_id: - - T1525 -modification_date: '2020-02-20' -name: GCP GCR container uploaded -original_authors: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/create_local_admin_accounts_using_net_exe.yml b/detections/create_local_admin_accounts_using_net_exe.yml new file mode 100644 index 0000000000..dd96f19f68 --- /dev/null +++ b/detections/create_local_admin_accounts_using_net_exe.yml @@ -0,0 +1,36 @@ +name: Create local admin accounts using net exe +id: b89919ed-fe5f-492c-b139-151bb162040e +version: 3 +date: '2020-03-16' +description: This search looks for the creation of local administrator accounts using + net.exe. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.user) as + user values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe + OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* + OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + |`create_local_admin_accounts_using_net_exe_filter` ' +known_false_positives: Administrators often leverage net.exe to create admin accounts. +tags: + analytics_story: + - DHS Report TA18-074A + mitre_attack_id: + - T1059 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/create_local_admin_via_net.yml b/detections/create_local_admin_via_net.yml deleted file mode 100644 index 624c8466c1..0000000000 --- a/detections/create_local_admin_via_net.yml +++ /dev/null @@ -1,110 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-03-28' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for the creation of local administrator accounts using - net.exe. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Net.exe was used to create local administrator accounts - on $dest$. - rule_title: Local administrator account created on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - create_local_admin_via_net_filter - schedule: - cron_schedule: 0 8 * * * - earliest_time: -1440m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) - as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by - Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_via_net_filter`' - suppress: - suppress_fields: dest, process_name - suppress_period: 86400s -eli5: Net.exe is a built-in Windows command-line tool that can be used to add, display, - or modify user accounts. While Microsoft administrators use this tool to manage - user groups, threat actors often leverage it to create local admin accounts to maintain - persistence. In this search, we are looking for the execution of process net.exe - with command-line parameters such as `localgroup`, `add`, or `user` that may correspond - to the creation of local admin accounts or setting user/group properties. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: b89919ed-fe5f-492c-b139-151bb162040e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Administrators often leverage net.exe to create admin accounts. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Command-Line Interface - - Persistence - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Create local admin accounts using net.exe -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/create_or_delete_network_shares.yml b/detections/create_or_delete_network_shares.yml deleted file mode 100644 index c7b1463d33..0000000000 --- a/detections/create_or_delete_network_shares.yml +++ /dev/null @@ -1,120 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-06-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for the creation or deletion of hidden shares using - net.exe. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest,process_name - rule_description: Net.exe was used to create or delete hidden network shares - by $user$ on $dest$ - rule_title: Hidden File shares created/deleted on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - create_or_delete_windows_shares_filter - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) - as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by - Processes.process Processes.process_name Processes.dest - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* - | `create_or_delete_windows_shares_filter`' - suppress: - suppress_fields: dest,process_name - suppress_period: 86400s -eli5: In this search, we are looking for the command-line execution of net.exe with - command-line parameters such as `net`, `share`, or `delete` that may correspond to - the creation/deletion of windows drive shares. Net.exe is a built-in command-line tool - on Windows that can be used to create, delete, and manage shared resources on the computer, - both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, - attackers also leverage it to create and delete (hidden) file shares by appending - "$" after the name of the share. Since the creation/deletion of hidden shares is a - special case of detecting share creation/deletion we have commented out - the regex that adds that additional matching criteria. If only hidden share detection is desired - add `| regex process="\S+[$]"` before the last pipe in the search. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: qw9919ed-fe5f-492c-b139-151bb162140e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Administrators often leverage net.exe to create or delete network - shares. You should verify that the activity was intentional and is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - mitre_technique_id: - - T1077 - - T1126 - nist: - - PR.PT - - DE.CM -modification_date: '2020-01-20' -name: Create or delete windows shares using net.exe -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://attack.mitre.org/techniques/T1077/ - - https://attack.mitre.org/techniques/T1126/ -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' diff --git a/detections/create_or_delete_windows_shares_using_net_exe.yml b/detections/create_or_delete_windows_shares_using_net_exe.yml new file mode 100644 index 0000000000..5e66d89735 --- /dev/null +++ b/detections/create_or_delete_windows_shares_using_net_exe.yml @@ -0,0 +1,36 @@ +name: Create or delete windows shares using net exe +id: qw9919ed-fe5f-492c-b139-151bb162140e +version: 4 +date: '2020-01-20' +description: This search looks for the creation or deletion of hidden shares using + net.exe. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: +- https://attack.mitre.org/techniques/T1077/ +- https://attack.mitre.org/techniques/T1126/ +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.user) as + user values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe + OR Processes.process_name=net1.exe) by Processes.process Processes.process_name + Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` ' +known_false_positives: Administrators often leverage net.exe to create or delete network + shares. You should verify that the activity was intentional and is legitimate. +tags: + analytics_story: + - Hidden Cobra Malware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/create_remote_thread_into_lsass.yml b/detections/create_remote_thread_into_lsass.yml index 91afff13fc..ed579f304e 100644 --- a/detections/create_remote_thread_into_lsass.yml +++ b/detections/create_remote_thread_into_lsass.yml @@ -1,86 +1,35 @@ -asset_type: Windows -confidence: high -creation_date: '2019-12-06' -data_metadata: - data_eventtypes: - - wineventlog_sysmon - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: Detect remote thread creation into LSASS consistent with credential dumping. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Possible attempt at credential dumping was detected on $dest$. - rule_title: remote thread creation into LSASS on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - macros: - - sysmon - - create_remote_thread_into_lsass_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` EventID=8 TargetImage=*lsass.exe - | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId - | rename Computer as dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_remote_thread_into_lsass_filter`' - suppress: - suppress_fields: dest, TargetProcessId - suppress_period: 86400s -eli5: This search detects the creation of a remote thread into LSASS (Local Security Authority Subsystem Service). - This technique can be used by attackers to inject code into LSASS and dump the memory in order to obtain credentials. -entities: - - dest -how_to_implement: 'This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.' -id: 67d4dbef-9564-4699-8da8-03a151529edc -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_technique_id: - - T1003 - mitre_attack: - - Credential Access - - Credential Dumping - nist: - - DE.CM -modification_date: '2019-12-06' name: Create Remote Thread into LSASS -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +id: 67d4dbef-9564-4699-8da8-03a151529edc +version: 1 +date: '2019-12-06' +description: Detect remote thread creation into LSASS consistent with credential dumping. +how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, which + includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +type: ESCU references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: "1" +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime + max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename + Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `create_remote_thread_into_lsass_filter`' +known_false_positives: Other tools can access LSASS for legitimate reasons and generate + an event. In these cases, tweaking the search may help eliminate noise. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/creation_of_shadow_copy.yml b/detections/creation_of_shadow_copy.yml index f151457714..cf3cf3bd5c 100644 --- a/detections/creation_of_shadow_copy.yml +++ b/detections/creation_of_shadow_copy.yml @@ -1,91 +1,38 @@ -asset_type: Endpoint -confidence: high -creation_date: '2019-12-10' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest, process_name, process - rule_description: An attempt to create a shadow copy with Ntdsutil, Vssadmin, or Wmic was detected on $dest$. - rule_title: Attempted Credential Dump of ntds.dit from $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 60 - macros: - - creation_of_shadow_copy_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) OR (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) - OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) - by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `creation_of_shadow_copy_filter`' - suppress: - suppress_fields: process_name, dest - suppress_period: 86400s -eli5: The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. - That's why attackers will first create a shadow copy before exfiltrating the file. This search detects the creation - of a shadow copy using Ntdsutil, Vssadmin, or Wmic. -entities: - - dest +name: Creation of Shadow Copy +id: eb120f5f-b879-4a63-97c1-93352b5df844 +version: 1 +date: '2019-12-10' +description: Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create + a shadow copy. how_to_implement: You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: eb120f5f-b879-4a63-97c1-93352b5df844 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.CM -modification_date: '2019-12-10' -name: Creation of Shadow Copy -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +type: ESCU references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe + Processes.process=*ntds* Processes.process=*create*) OR (Processes.process_name=vssadmin.exe + Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe + Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user + Processes.process_name Processes.process Processes.parent_process Processes.process_id + Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter`' +known_false_positives: Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic + will create false positives. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml b/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml index 0608ed6938..99dd07169e 100644 --- a/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml +++ b/detections/creation_of_shadow_copy_with_wmic_and_powershell.yml @@ -1,87 +1,35 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2019-12-10' -data_metadata: - data_eventtypes: - - powershell scriptblock logging - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search detects the use of wmic and Powershell to create a shadow copy. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest - rule_description: An attempt to create a shadow copy with wmic and Powershell was detected on $dest$. - rule_title: Attempted Credential Dump of ntds.dit on $dest$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - macros: - - sysmon - - creation_of_shadow_copy_with_wmic_and_powershell_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` Message=*win32_shadowcopy* Message=*Create* - | stats count min(_time) as firstTime max(_time) as lastTime by dvc User EventCode Message - | rename User as user, dvc as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `creation_of_shadow_copy_with_wmic_and_powershell_filter` ' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. - That's why attackers create a shadow copy before exfiltrating the file. This search detects the creation - of a shadow copy using wmic, which is executed by Powershell. -entities: - - dest -how_to_implement: You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -id: 2ed8b538-d284-449a-be1d-82ad1dbd186b -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: Legtimate administrator usage of wmic to create a shadow copy. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.CM -modification_date: '2019-12-10' name: Creation of Shadow Copy with wmic and powershell -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +id: 2ed8b538-d284-449a-be1d-82ad1dbd186b +version: 1 +date: '2019-12-10' +description: This search detects the use of wmic and Powershell to create a shadow + copy. +how_to_implement: You must enable Powershell scriptblock logging in order to detect + this attack.This search uses an input macro named `sysmon`. We strongly recommend + that you specify your environment-specific configurations (index, source, sourcetype, + etc.) for Windows Sysmon logs. Replace the macro definition with configurations + for your Splunk environment. The search also uses a post-filter macro designed to + filter out known false positives. +type: ESCU references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '`sysmon` Message=*win32_shadowcopy* Message=*Create* | stats count min(_time) + as firstTime max(_time) as lastTime by dvc User EventCode Message | rename User + as user, dvc as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `creation_of_shadow_copy_with_wmic_and_powershell_filter` ' +known_false_positives: Legtimate administrator usage of wmic to create a shadow copy. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/cred_dump_via_copy_from_shadowcopy.yml b/detections/cred_dump_via_copy_from_shadowcopy.yml deleted file mode 100644 index a7250a1a46..0000000000 --- a/detections/cred_dump_via_copy_from_shadowcopy.yml +++ /dev/null @@ -1,90 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2019-12-10' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: This search detects credential dumping using copy command from a shadow copy. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest, process_name, process - rule_description: credential dumping using copy command was detected on $dest$. - rule_title: Attempted Credential Dump using copy command from $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 40 - macros: - - cred_dump_via_copy_from_shadowcopy_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) - by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `cred_dump_via_copy_from_shadowcopy_filter`' - suppress: - suppress_fields: user, dest, process - suppress_period: 86400s -eli5: The file system, security, sam and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied. - But it is possible by creating first a shadow copy and then copy it from the shadow copy. This search will detect this attack of - credential dumping. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: d8c406fe-23d2-45f3-a983-1abe7b83ff3b -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: unknown -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.CM -modification_date: '2019-12-10' -name: Credential Dumping via Copy Command from Shadow Copy -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/cred_dump_via_symlink_shadowcopy.yml b/detections/cred_dump_via_symlink_shadowcopy.yml deleted file mode 100644 index 3a0c6977a5..0000000000 --- a/detections/cred_dump_via_symlink_shadowcopy.yml +++ /dev/null @@ -1,89 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2019-12-10' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: This search detects the creation of a symlink to a shadow copy. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest, process_name, process - rule_description: credential dumping using symlink on $dest$. - rule_title: Attempted Credential Dump using symlink from $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 40 - macros: - - cred_dump_via_symlink_shadowcopy_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* - by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `cred_dump_via_symlink_shadowcopy_filter` ' - suppress: - suppress_fields: user, dest, process - suppress_period: 86400s -eli5: The file system, security, sam, and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied, but it can be done by creating shadow copy and then create a symlink to the shadow copy. This search will detect this attack of - credential dumping. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: c5eac648-fae0-4263-91a6-773df1f4c903 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: unknown -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.CM -modification_date: '2019-12-10' -name: Credential Dumping via Symlink to Shadow Copy -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/credential_dumping_via_copy_command_from_shadow_copy.yml b/detections/credential_dumping_via_copy_command_from_shadow_copy.yml new file mode 100644 index 0000000000..0031a9b18a --- /dev/null +++ b/detections/credential_dumping_via_copy_command_from_shadow_copy.yml @@ -0,0 +1,36 @@ +name: Credential Dumping via Copy Command from Shadow Copy +id: d8c406fe-23d2-45f3-a983-1abe7b83ff3b +version: 1 +date: '2019-12-10' +description: This search detects credential dumping using copy command from a shadow + copy. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe + (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* + OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) + by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process + Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` ' +known_false_positives: unknown +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/credential_dumping_via_symlink_to_shadow_copy.yml b/detections/credential_dumping_via_symlink_to_shadow_copy.yml new file mode 100644 index 0000000000..2be920fab2 --- /dev/null +++ b/detections/credential_dumping_via_symlink_to_shadow_copy.yml @@ -0,0 +1,34 @@ +name: Credential Dumping via Symlink to Shadow Copy +id: c5eac648-fae0-4263-91a6-773df1f4c903 +version: 1 +date: '2019-12-10' +description: This search detects the creation of a symlink to a shadow copy. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe + Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest + Processes.user Processes.process_name Processes.process Processes.parent_process + Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` ' +known_false_positives: unknown +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/cscript_via_cmd.yml b/detections/cscript_via_cmd.yml deleted file mode 100644 index 3981c1527b..0000000000 --- a/detections/cscript_via_cmd.yml +++ /dev/null @@ -1,114 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-10-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for the execution of the cscript.exe or wscript.exe - processes, with a parent of cmd.exe. The search will return the count, the first - and last time this execution was seen on a machine, the user, and the destination - of the machine -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, parent_process - rule_description: Potentially malicious script execution detected. - rule_title: 'Command prompt is executing scripts on $dest$ using $process_name$ ' - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR - Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name - Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process_name - suppress_period: 86400s -eli5: Attackers often leverage various scripting languages to execute their attacks. - In a Windows environment, the Windows Script Host is the tool that interprets the - scripts and is included in all modern versions of Windows. The Windows Script Host - is available as a command-line tool called "cscript.exe" or "wscript.exe." To detect - this behavior, the search looks for process-creation events for cscript.exe or wscript.exe - with a parent process of cmd.exe. The search will return the count, the first and - last times this behavior was seen on a destination machine, and user and process - information. -entities: - - dest - - process_name - - user -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -id: b89919ed-fe5f-492c-b139-95dbb162039e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Some legitimate applications may exhibit this behavior. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Exploitation - mitre_attack: - - Execution - - Command-Line Interface - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-02' -name: Detect Use of cmd.exe to Launch Script Interpreters -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/delete_shadow_copies.yml b/detections/delete_shadow_copies.yml deleted file mode 100644 index 481e6b06b6..0000000000 --- a/detections/delete_shadow_copies.yml +++ /dev/null @@ -1,115 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-02-17' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The vssadmin.exe utility is used to interact with the Volume Shadow Copy - Service. Wmic is an interface to the Windows Management Instrumentation. This - search looks for either of these tools being used to delete shadow copies. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: Using $process_name$ to delete shadow copies is common behavior - by ransomware. This activity was observed on $dest$ - rule_title: Deleting Shadow Copies on $dest$ with $process_name$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe - OR Processes.process_name=wmic.exe) by Processes.user Processes.process_name - Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*delete* AND process=*shadow*' - suppress: - suppress_fields: dest, user - suppress_period: 14400s -eli5: This search looks for execution of vssadmin or wmic with both the "delete" and - "shadows" parameters passed on the command-line. The two arguments are searched - for separately because we can't predict the number of spaces between the words on - the command-line. The search will return the number of times this activity was observed, - and the times of the first and last event. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: b89919ed-ee5f-492c-b139-95dbb162039e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped - with modern versions of windows. They may be used by administrators to legitimately - delete old backup copies, although this is typically rare. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - - CIS 10 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Impact - mitre_technique_id: - - T1490 - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2020-04-17' -name: Deleting Shadow Copies -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.1' diff --git a/detections/deleting_shadow_copies.yml b/detections/deleting_shadow_copies.yml new file mode 100644 index 0000000000..62f95fe846 --- /dev/null +++ b/detections/deleting_shadow_copies.yml @@ -0,0 +1,40 @@ +name: Deleting Shadow Copies +id: b89919ed-ee5f-492c-b139-95dbb162039e +version: 2 +date: '2020-04-17' +description: The vssadmin.exe utility is used to interact with the Volume Shadow Copy + Service. Wmic is an interface to the Windows Management Instrumentation. This + search looks for either of these tools being used to delete shadow copies. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe + OR Processes.process_name=wmic.exe) by Processes.user Processes.process_name Processes.parent_process_name + Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | search process=*delete* AND process=*shadow* + | `deleting_shadow_copies_filter`' +known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped + with modern versions of windows. They may be used by administrators to legitimately + delete old backup copies, although this is typically rare. +tags: + analytics_story: + - Windows Log Manipulation + - SamSam Ransomware + - Ransomware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 10 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/deletion_network_acl.yml b/detections/deletion_network_acl.yml deleted file mode 100644 index a87195b53c..0000000000 --- a/detections/deletion_network_acl.yml +++ /dev/null @@ -1,88 +0,0 @@ -asset_type: AWS Instance -confidence: medium -creation_date: '2017-01-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: Enforcing network-access controls is one of the defensive mechanisms - used by cloud administrators to restrict access to a cloud instance. After the attacker - has gained control of the AWS console by compromising an admin account, they can - delete a network ACL and gain access to the instance from anywhere. This search - will query the CloudTrail logs to detect users deleting network ACLs. -detect: - splunk: - correlation_rule: - notable: - nes_fields: arn, eventName - rule_description: AWS network ACL has been deleted by $arn$. - rule_title: AWS Network ACL deleted by $arn$ - risk: - risk_object: arn - risk_object_type: - - user - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -1d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn - as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) - values(errorCode) values(userAgent) values(userIdentity.*) by src userName - arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - suppress: - suppress_fields: arn - suppress_period: 14400s -eli5: The search looks for CloudTrail events to detect whether any network ACLs have - been deleted and gives you values of error messages and error codes (if any), user - details, user source IP, the user who initiated this request, and the name of the - event. -entities: - - arn -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: ada0f478-84a8-4641-a3f1-d82362d6fd75 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible that a user has legitimately deleted a network - ACL. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 11 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - nist: - - DE.DP - - DE.AE -modification_date: '2017-01-10' -name: AWS Network Access Control List Deleted -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/detect_activity_related_to_pass_the_hash_attacks.yml b/detections/detect_activity_related_to_pass_the_hash_attacks.yml new file mode 100644 index 0000000000..dc7b6025cb --- /dev/null +++ b/detections/detect_activity_related_to_pass_the_hash_attacks.yml @@ -0,0 +1,36 @@ +name: Detect Activity Related to Pass the Hash Attacks +id: f5939373-8054-40ad-8c64-cec478a22a4b +version: 3 +date: '2020-01-09' +description: This search looks for specific authentication events from the Windows + Security Event logs to detect potential attempts at using the Pass-the-Hash technique. +how_to_implement: To successfully implement this search, you must ingest your Windows + Security Event logs and leverage the latest TA for Windows. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`wineventlog_security` EventCode=4624 (Logon_Type=3 LogonProcessName=NtLmSsp + WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (EventCode=4624 + Logon_Type=9 LogonProcessName=seclogo) | stats count min(_time) as firstTime max(_time) + as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` ' +known_false_positives: Legitimate logon activity by authorized NTLM systems may be + detected by this search. Please investigate as appropriate. +tags: + analytics_story: + - Lateral Movement + mitre_attack_id: + - T1075 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 16 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: access + asset_type: Endpoint diff --git a/detections/detect_api_activity_from_users_without_mfa.yml b/detections/detect_api_activity_from_users_without_mfa.yml new file mode 100644 index 0000000000..b113052716 --- /dev/null +++ b/detections/detect_api_activity_from_users_without_mfa.yml @@ -0,0 +1,56 @@ +name: Detect API activity from users without MFA +id: 2a9b80d3-6340-4345-w5ad-212bf5d1dac4 +version: 1 +date: '2018-05-17' +description: This search looks for CloudTrail events where a user logged into the + AWS account, is making API calls and has not enabled Multi Factor authentication. + Multi factor authentication adds a layer of security by forcing the users to type + a unique authentication code from an approved authentication device when they access + AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged + IAM users. +how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Leverage the support search `Create a list of approved AWS service accounts`: + run it once every 30 days to create a list of service accounts and validate them.\ + + This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) + that are not yet supported by ES Incident Review and therefore cannot be viewed + when a notable event is raised. These fields contribute additional context to the + notable. To see the additional metadata, add the following fields, if not already + present, to Incident Review - Event Attributes (Configure > Incident Management + > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** + eventName\ + + 1. \ + + 1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ + + 1. \ + + 1. **Label:** AWS User Type, **Field:** userIdentity.type\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false + | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity + as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) + as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | + `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter`' +known_false_positives: Many service accounts configured within an AWS infrastructure + do not have multi factor authentication enabled. Please ignore the service accounts, + if triggered and instead add them to the aws_service_accounts.csv file to fine tune + the detection. It is also possible that the search detects users in your environment + using Single Sign-On systems, since the MFA is not handled by AWS. +tags: + analytics_story: + - AWS User Monitoring + cis20: + - CIS 16 + nist: + - DE.DP + - PR.AC + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml new file mode 100644 index 0000000000..9cc94a66ce --- /dev/null +++ b/detections/detect_attackers_scanning_for_vulnerable_jboss_servers.yml @@ -0,0 +1,29 @@ +name: Detect attackers scanning for vulnerable JBoss servers +id: 104658f4-afdc-499e-9719-17243f982681 +version: 1 +date: '2017-09-23' +description: This search looks for specific GET or HEAD requests to web servers that + are indicative of reconnaissance attempts to identify vulnerable JBoss servers. + JexBoss is described as the exploit tool of choice for this malicious activity. +how_to_implement: You must be ingesting data from the web server or network traffic + that contains web specific information, and populating the Web data model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") + AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" + OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`' +known_false_positives: It's possible for legitimate HTTP requests to be made to URLs + containing the suspicious paths. +tags: + analytics_story: + - JBoss Vulnerability + - SamSam Ransomware + mitre_attack_id: + - T1082 + kill_chain_phases: + - Reconnaissance + security_domain: network + asset_type: Web Server diff --git a/detections/detect_aws_api_activities_from_unapproved_accounts.yml b/detections/detect_aws_api_activities_from_unapproved_accounts.yml new file mode 100644 index 0000000000..6539c00e6e --- /dev/null +++ b/detections/detect_aws_api_activities_from_unapproved_accounts.yml @@ -0,0 +1,60 @@ +name: Detect AWS API Activities From Unapproved Accounts +id: ada0f478-84a8-4641-a3f1-d82362d4bd55 +version: 1 +date: '2018-03-13' +description: This search looks for successful CloudTrail activity by user accounts + that are not listed in the identity table or `aws_service_accounts.csv`. It returns + event names and count, as well as the first and last time a specific user or service + is detected, grouped by users. +how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You must also populate the `identity_lookup_expanded` lookup shipped with + the Asset and Identity framework to be able to look up users in your identity table + in Enterprise Security (ES). Leverage the support search called "Create a list of + approved AWS service accounts": run it once every 30 days to create and validate + a list of service accounts.\ + + This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet + supported by ES Incident Review and therefore cannot be viewed when a notable event + is raised. These fields contribute additional context to the notable. To see the + additional metadata, add the following fields, if not already present, to Incident + Review - Event Attributes (Configure > Incident Management > Incident Review Settings + > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ + + 1. \ + + 1. **Label:** First Time, **Field:** firstTime\ + + 1. \ + + 1. **Label:** Last Time, **Field:** lastTime\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` errorCode=success | rename userName as identity | search NOT + [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup + aws_service_accounts | fields identity] | rename identity as user | stats count + min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by + user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `detect_aws_api_activities_from_unapproved_accounts_filter`' +known_false_positives: It's likely that you'll find activity detected by users/service + accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` + file. If the user is a legitimate service account, update the `aws_service_accounts.csv` + table with that entry. +tags: + analytics_story: + - AWS User Monitoring + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.CM + - PR.AC + - ID.AM + security_domain: access + asset_type: AWS Instance diff --git a/detections/detect_aws_console_login_by_user_from_new_city.yml b/detections/detect_aws_console_login_by_user_from_new_city.yml new file mode 100644 index 0000000000..a3797cc46e --- /dev/null +++ b/detections/detect_aws_console_login_by_user_from_new_city.yml @@ -0,0 +1,41 @@ +name: Detect AWS Console Login by User from New City +id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events wherein a console login event + by a user was recorded within the last hour, then compares the event to a lookup + file of previously seen users (by ARN values) who have logged into the console. + The alert is fired if the user has logged into the console for the first time within + the last hour +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the "Previously seen users in CloudTrail" support search only once to + create a baseline of previously seen IAM users within the last 30 days. Run "Update + previously seen users in CloudTrail" hourly (or more frequently depending on how + often you run the detection searches) to refresh the baselines. +type: ESCU +author: Jason Brewer, Splunk +search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by user City | join user type=outer [| inputlookup + previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen + by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), + "@d"), "New City","Previously Seen City") | eval UserData=if(earliestseen >= relative_time(now(), + "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New City" + AND UserData="Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| + `security_content_ctime(earliestseen)` | table user City userStatus firstTime lastTime + earliestseen | `detect_aws_console_login_by_user_from_new_city_filter`' +known_false_positives: When a legitimate new user logins for the first time, this + activity will be detected. Check how old the account is and verify that the user + activity is legitimate. +tags: + analytics_story: + - Suspicious AWS Login Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_aws_console_login_by_user_from_new_country.yml b/detections/detect_aws_console_login_by_user_from_new_country.yml new file mode 100644 index 0000000000..60fccc3b0a --- /dev/null +++ b/detections/detect_aws_console_login_by_user_from_new_country.yml @@ -0,0 +1,41 @@ +name: Detect AWS Console Login by User from New Country +id: 67bd3def-c41c-4bf6-837b-ae196b4257c6 +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events wherein a console login event + by a user was recorded within the last hour, then compares the event to a lookup + file of previously seen users (by ARN values) who have logged into the console. + The alert is fired if the user has logged into the console for the first time within + the last hour +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the "Previously seen users in CloudTrail" support search only once to + create a baseline of previously seen IAM users within the last 30 days. Run "Update + previously seen users in CloudTrail" hourly (or more frequently depending on how + often you run the detection searches) to refresh the baselines. +type: ESCU +author: Jason Brewer, Splunk +search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by user Country | join user type=outer [| + inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen + by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), + "@d"), "New Country","Previously Seen Country") | eval UserData=if(earliestseen + >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | + where userStatus="New Country" AND UserData="Old User" | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`|`security_content_ctime(earliestseen)` | table + user Country userStatus firstTime lastTime earliestseen | `detect_aws_console_login_by_user_from_new_country_filter`' +known_false_positives: When a legitimate new user logins for the first time, this + activity will be detected. Check how old the account is and verify that the user + activity is legitimate. +tags: + analytics_story: + - Suspicious AWS Login Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_aws_console_login_by_user_from_new_region.yml b/detections/detect_aws_console_login_by_user_from_new_region.yml new file mode 100644 index 0000000000..47ac42c4f2 --- /dev/null +++ b/detections/detect_aws_console_login_by_user_from_new_region.yml @@ -0,0 +1,41 @@ +name: Detect AWS Console Login by User from New Region +id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026 +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events wherein a console login event + by a user was recorded within the last hour, then compares the event to a lookup + file of previously seen users (by ARN values) who have logged into the console. + The alert is fired if the user has logged into the console for the first time within + the last hour +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the "Previously seen users in CloudTrail" support search only once to + create a baseline of previously seen IAM users within the last 30 days. Run "Update + previously seen users in CloudTrail" hourly (or more frequently depending on how + often you run the detection searches) to refresh the baselines. +type: ESCU +author: Jason Brewer, Splunk +search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by user Region | join user type=outer [| + inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen + by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), + "@d"), "New Region","Previously Seen Region") | eval UserData=if(earliestseen >= + relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where + userStatus="New Region" AND UserData="Old User" | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | `security_content_ctime(earliestseen)` | table + user Region userStatus firstTime lastTime earliestseen | `detect_aws_console_login_by_user_from_new_region_filter`' +known_false_positives: When a legitimate new user logins for the first time, this + activity will be detected. Check how old the account is and verify that the user + activity is legitimate. +tags: + analytics_story: + - Suspicious AWS Login Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_credential_dumping_through_LSASS_access.yml b/detections/detect_credential_dumping_through_LSASS_access.yml index f3dc4d36c7..bcff7da23a 100644 --- a/detections/detect_credential_dumping_through_LSASS_access.yml +++ b/detections/detect_credential_dumping_through_LSASS_access.yml @@ -1,99 +1,41 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-08-28' -data_metadata: - data_eventtypes: - - wineventlog_sysmon - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search looks for reading lsass memory consistent with credential dumping. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest - rule_description: Possible attempt at credential dumping was detected on $dest$. - rule_title: Detect reading lsass memory on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - macros: - - sysmon - - detect_credential_dumping_through_LSASS_access_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) - | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess - | rename Computer as dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `detect_credential_dumping_through_LSASS_access_filter`' - suppress: - suppress_fields: user, dest, ProcessName - suppress_period: 86400s -eli5: This search looks for LSASS access using Credential Dumping tools by detecting Process access with - Sysmon logs (EventCode 10), TargetImage lsass.exe and GrantedAccess 0x1410 or 0x1010. - This will for example detect the use of sekurlsa::logonpasswords in Mimikatz. -entities: - - dest -how_to_implement: 'This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.' -id: 2c365e57-4414-4540-8dc0-73ab10729996 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: The activity may be legitimate. Other tools can access lsass - for legitimate reasons, and it's possible this event could be generated - in those cases. In these cases, false positives should be fairly obvious and you - may need to tweak the search to eliminate noise. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - PR.IP - - PR.AC - - DE.CM -modification_date: '2019-12-03' name: Detect Credential Dumping through LSASS access -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +id: 2c365e57-4414-4540-8dc0-73ab10729996 +version: 3 +date: '2019-12-03' +description: This search looks for reading lsass memory consistent with credential + dumping. +how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which + includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: "3" +author: Patrick Bareiss, Splunk +search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) + | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, + SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename + Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `detect_credential_dumping_through_lsass_access_filter` ' +known_false_positives: The activity may be legitimate. Other tools can access lsass + for legitimate reasons, and it's possible this event could be generated in those + cases. In these cases, false positives should be fairly obvious and you may need + to tweak the search to eliminate noise. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 16 + nist: + - PR.IP + - PR.AC + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml b/detections/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml new file mode 100644 index 0000000000..cc832e4c61 --- /dev/null +++ b/detections/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml @@ -0,0 +1,54 @@ +name: Detect DNS requests to Phishing Sites leveraging EvilGinx2 +id: 24dd17b1-e2fb-4c31-878c-d4f226595bfa +version: 1 +date: '2019-04-29' +description: This search looks for DNS requests for phishing domains that are leveraging + EvilGinx tools to mimic websites. +how_to_implement: "You need to ingest data from your DNS logs in the Network_Resolution\ + \ datamodel. Specifically you must ingest the domain that is being queried and the\ + \ IP of the host originating the request. Ideally, you should also be ingesting\ + \ the answer to the query and the query type. This approach allows you to also create\ + \ your own localized passive DNS capability which can aid you in future investigations.\ + \ You will have to add legitimate domain names to the `legit_domains.csv` file shipped\ + \ with the app. \\\n **Splunk>Phantom Playbook Integration**\\\nIf Splunk>Phantom\ + \ is also configured in your environment, a Playbook called `Lets Encrypt Domain\ + \ Investigate` can be configured to run when any results are found by this detection\ + \ search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,\ + \ add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response\ + \ Actions when configuring this detection search, and set the corresponding Playbook\ + \ to active. \\\n(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\\\ + \n" +type: ESCU +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by + DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" + | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` + OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` + OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` + | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer + [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web + by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" + | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`' +known_false_positives: If a known good domain is not listed in the legit_domains.csv + file, then the search could give you false postives. Please update that lookup file + to filter out DNS requests to legitimate domains. +tags: + analytics_story: + - Common Phishing Frameworks + mitre_attack_id: + - T1192 + kill_chain_phases: + - Delivery + - Command and Control + cis20: + - CIS 8 + - CIS 7 + nist: + - ID.AM + - PR.DS + - PR.IP + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_excessive_account_lockouts_from_endpoint.yml b/detections/detect_excessive_account_lockouts_from_endpoint.yml new file mode 100644 index 0000000000..bc35744147 --- /dev/null +++ b/detections/detect_excessive_account_lockouts_from_endpoint.yml @@ -0,0 +1,40 @@ +name: Detect Excessive Account Lockouts From Endpoint +id: c026e3dd-7e18-4abb-8f41-929e836efe74 +version: 3 +date: '2019-04-18' +description: This search identifies endpoints that have caused a relatively high number + of account lockouts in a short period. +how_to_implement: "You must ingest your Windows security event logs in the `Change`\ + \ datamodel under the nodename is `Account_Management`, for this search to execute\ + \ successfully. Please consider updating the cron schedule and the count of lockouts\ + \ you want to monitor, according to your environment. \\\n **Splunk>Phantom Playbook\ + \ Integration**\\\nIf Splunk>Phantom is also configured in your environment, a Playbook\ + \ called \"Excessive Account Lockouts Enrichment and Response\" can be configured\ + \ to run when any results are found by this detection search. The Playbook executes\ + \ the Contextual and Investigative searches in this Story, conducts additional information\ + \ gathering on Windows endpoints, and takes a response action to shut down the affected\ + \ endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,\ + \ add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response\ + \ Actions when configuring this detection search, and set the corresponding Playbook\ + \ to active. \\\n(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\\\ + \n" +type: ESCU +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management + All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` + |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`' +known_false_positives: It's possible that a widely used system, such as a kiosk, could + cause a large number of account lockouts. +tags: + analytics_story: + - Account Monitoring and Controls + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - PR.IP + security_domain: access + asset_type: Windows diff --git a/detections/detect_excessive_user_account_lockouts.yml b/detections/detect_excessive_user_account_lockouts.yml new file mode 100644 index 0000000000..65c3bb03d3 --- /dev/null +++ b/detections/detect_excessive_user_account_lockouts.yml @@ -0,0 +1,31 @@ +name: Detect Excessive User Account Lockouts +id: 95a7f9a5-6096-437e-a19e-86f42ac609bd +version: 2 +date: '2019-03-01' +description: This search detects user accounts that have been locked out a relatively + high number of times in a short period. +how_to_implement: ou must ingest your Windows security event logs in the `Change` + datamodel under the nodename is `Account_Management`, for this search to execute + successfully. Please consider updating the cron schedule and the count of lockouts + you want to monitor, according to your environment. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management + All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` + |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter`' +known_false_positives: It is possible that a legitimate user is experiencing an issue + causing multiple account login failures leading to lockouts. +tags: + analytics_story: + - Account Monitoring and Controls + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - PR.IP + security_domain: access + asset_type: Windows diff --git a/detections/dyn_dns_queries.yml b/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml similarity index 53% rename from detections/dyn_dns_queries.yml rename to detections/detect_hosts_connecting_to_dynamic_domain_providers.yml index f72977089c..8c3fcfa8fb 100644 --- a/detections/dyn_dns_queries.yml +++ b/detections/detect_hosts_connecting_to_dynamic_domain_providers.yml @@ -1,50 +1,13 @@ -asset_type: Endpoint -baselines: [] -confidence: medium -creation_date: '2017-11-17' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: Detect hosts connecting to dynamic domain providers +id: c77162d3-f93c-45cc-80c8-22f6v5464g9f +version: 2 +date: '2020-01-16' description: Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. -detect: - splunk: - correlation_rule: - notable: - nes_fields: answer, src, query - rule_description: The search has detected a host making outbound queries to - Dynamic DNS providers - rule_title: Host $src$ detected to make a query to a Dynamic DNS provider - risk: - risk_object: src - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) - as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` - | `security_content_ctime(firstTime)` | `dynamic_dns_providers`' - suppress: - suppress_fields: src, query - suppress_period: 86400s -eli5: The search leverages an accelerated `Network_Resolution` data model to count - and list the values of resolved domains for each DNS query. It checks the results - against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` - by each host (DNS.src). -entities: - - src how_to_implement: 'First, you''ll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is @@ -70,42 +33,34 @@ how_to_implement: 'First, you''ll need to ingest data from your DNS operations. Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: c77162d3-f93c-45cc-80c8-22f6v5464g9f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk +type: ESCU +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer + min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query + | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` + | `detect_hosts_connecting_to_dynamic_domain_providers_filter`' known_false_positives: Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 8 - - CIS 12 - - CIS 13 +tags: + analytics_story: + - Data Protection + - Prohibited Traffic Allowed or Protocol Mismatch + - DNS Hijacking + - Suspicious DNS Traffic + - Dynamic DNS + - Command and Control kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Exfiltration - - Defense Evasion + - Command and Control + - Actions on Objectives + cis20: + - CIS 8 + - CIS 12 + - CIS 13 nist: - - PR.DS - - PR.PT - - DE.AE - - DE.CM -modification_date: '2020-01-16' -name: Detect hosts connecting to dynamic domain providers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' + - PR.DS + - PR.PT + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_large_outbound_icmp_packets.yml b/detections/detect_large_outbound_icmp_packets.yml new file mode 100644 index 0000000000..d5f2e70dfb --- /dev/null +++ b/detections/detect_large_outbound_icmp_packets.yml @@ -0,0 +1,48 @@ +name: Detect Large Outbound ICMP Packets +id: e9c102de-4d43-42a7-b1c8-8062ea297419 +version: 2 +date: '2018-06-01' +description: This search looks for outbound ICMP packets with a packet size larger + than 1,000 bytes. Various threat actors have been known to use ICMP as a command + and control channel for their attack infrastructure. Large ICMP packets from an + endpoint to a remote host may be indicative of this activity. +how_to_implement: 'In order to run this search effectively, we highly recommend that + you leverage the Assets and Identity framework. It is important that you have a + good understanding of how your network segments are designed and that you are able + to distinguish internal from external address space. Add a category named `internal` + to the CIDRs that host the company''s assets in the `assets_by_cidr.csv` lookup + file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. + More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. + This search also requires you to be ingesting your network traffic and populating + the Network_Traffic data model' +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime + latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from + datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category + !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes + > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` + | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) + | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`' +known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot + networking issues and ensure the proper flow of traffic. As such, it is possible + that a large ICMP packet could be perfectly legitimate. If large ICMP packets are + associated with command and control traffic, there will typically be a large number + of these packets observed over time. If the search is providing a large number of + false positives, you can modify the search to adjust the byte threshold or whitelist + specific IP addresses, as necessary. +tags: + analytics_story: + - Command and Control + mitre_attack_id: + - T1095 + kill_chain_phases: + - Command and Control + cis20: + - CIS 9 + - CIS 12 + nist: + - DE.AE + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_long_dns_txt_record_response.yml b/detections/detect_long_dns_txt_record_response.yml new file mode 100644 index 0000000000..12a52379a4 --- /dev/null +++ b/detections/detect_long_dns_txt_record_response.yml @@ -0,0 +1,47 @@ +name: Detect Long DNS TXT Record Response +id: 05437c07-62f5-452e-afdc-04dd44815bb9 +version: 1 +date: '2017-09-18' +description: This search is used to detect attempts to use DNS tunneling, by calculating + the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission + for data exfiltration, command and control, or evasion of security controls can + often be detected by noting unusually large volumes of DNS traffic. +how_to_implement: To successfully implement this search you need to ingest data from + your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, + this query requires that the DNS data model is populated with information regarding + the DNS record type that is being returned as well as the data in the answer section + of the protocol. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND + DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` + | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination + IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record + Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source + IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First + Time" "Last Time" | `detect_long_dns_txt_record_response_filter`' +known_false_positives: It's possible that legitimate TXT record responses can be long + enough to trigger this search. You can modify the packet threshold for this search + to help mitigate false positives. +tags: + analytics_story: + - Suspicious DNS Traffic + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + cis20: + - CIS 8 + - CIS 12 + - CIS 13 + nist: + - PR.DS + - PR.PT + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_malicious_requests_to_exploit_jboss_servers.yml b/detections/detect_malicious_requests_to_exploit_jboss_servers.yml new file mode 100644 index 0000000000..ba130244e6 --- /dev/null +++ b/detections/detect_malicious_requests_to_exploit_jboss_servers.yml @@ -0,0 +1,39 @@ +name: Detect malicious requests to exploit JBoss servers +id: c8bff7a4-11ea-4416-a27d-c5bca472913d +version: 1 +date: '2017-09-23' +description: This search is used to detect malicious HTTP requests crafted to exploit + jmx-console in JBoss servers. The malicious requests have a long URL length, as + the payload is embedded in the URL. +how_to_implement: You must ingest data from the web server or capture network data + that contains web specific information with solutions such as Bro or Splunk Stream, + and populating the Web data model +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") + by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" + AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, + lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`' +known_false_positives: No known false positives for this detection. +tags: + analytics_story: + - JBoss Vulnerability + - SamSam Ransomware + kill_chain_phases: + - Delivery + cis20: + - CIS 12 + - CIS 4 + - CIS 18 + nist: + - ID.RA + - PR.PT + - PR.IP + - DE.AE + - PR.MA + - DE.CM + security_domain: network + asset_type: Web Server diff --git a/detections/detect_mimikatz_using_loaded_images.yml b/detections/detect_mimikatz_using_loaded_images.yml index 156f87899b..df50d775d3 100644 --- a/detections/detect_mimikatz_using_loaded_images.yml +++ b/detections/detect_mimikatz_using_loaded_images.yml @@ -1,87 +1,38 @@ -asset_type: Windows -confidence: high -creation_date: '2019-12-03' -data_metadata: - data_eventtypes: - - wineventlog_sysmon - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search looks for reading loaded Images unique to credential dumping with Mimikatz. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Possible attempt at credential dumping was detected on $dest$. - rule_title: Detect Mimikatz using loaded images on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - macros: - - sysmon - - detect_mimikatz_using_loaded_images_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image - | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll - | rename Computer as dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter`' - suppress: - suppress_fields: dest, Image - suppress_period: 86400s -eli5: This search looks for loaded images (dll) unique for Mimikatz using Sysmon EventCode 7 logs. -entities: - - dest -how_to_implement: 'This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.' -id: 29e307ba-40af-4ab2-91b2-3c6b392bbba0 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: Other tools can import the same DLLs. These tools should be - part of a whtelist. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 6 - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 - nist: - - DE.AE - - DE.CM -modification_date: '2019-12-03' name: Detect Mimikatz Using Loaded Images -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +id: 29e307ba-40af-4ab2-91b2-3c6b392bbba0 +version: 1 +date: '2019-12-03' +description: This search looks for reading loaded Images unique to credential dumping + with Mimikatz. +how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which + includes EventCode 7 with powershell.exe. This search uses an input macro named + `sysmon`. We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +type: ESCU references: - - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html -security_domain: endpoint -spec_version: 2 -type: splunk -version: "1" +- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html +author: Patrick Bareiss, Splunk +search: '`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) + as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll + ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename + Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `detect_mimikatz_using_loaded_images_filter`' +known_false_positives: Other tools can import the same DLLs. These tools should be + part of a whtelist. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 6 + - CIS 8 + nist: + - DE.AE + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/detect_mimikatz_via_powershell_and_eventcode_4703.yml b/detections/detect_mimikatz_via_powershell_and_eventcode_4703.yml new file mode 100644 index 0000000000..cbcdf66cfb --- /dev/null +++ b/detections/detect_mimikatz_via_powershell_and_eventcode_4703.yml @@ -0,0 +1,41 @@ +name: Detect Mimikatz Via PowerShell And EventCode 4703 +id: 98917be2-bfc8-475a-8618-a9bb06575188 +version: 2 +date: '2019-02-27' +description: This search looks for PowerShell requesting privileges consistent with + credential dumping. +how_to_implement: 'You must be ingesting Windows Security logs. You must also enable + the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. + Additionally, this search requires you to enable your Group Management Audit Logs + in your Local Windows Security Policy and to be ingesting those logs. More information + on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. + Finally, please make sure that the local administrator group name is "Administrators" + to be able to look for the right group membership changes.' +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '`wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex + field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where + privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime + by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" + | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `detect_mimikatz_via_powershell_and_eventcode_4703_filter`' +known_false_positives: The activity may be legitimate. PowerShell is often used by + administrators to perform various tasks, and it's possible this event could be generated + in those cases. In these cases, false positives should be fairly obvious and you + may need to tweak the search to eliminate noise. +tags: + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 16 + nist: + - PR.IP + - PR.AC + - DE.CM + security_domain: access + asset_type: Windows diff --git a/detections/detect_mshta_exe_running_scripts_in_command_line_arguments.yml b/detections/detect_mshta_exe_running_scripts_in_command_line_arguments.yml new file mode 100644 index 0000000000..5d592e10d9 --- /dev/null +++ b/detections/detect_mshta_exe_running_scripts_in_command_line_arguments.yml @@ -0,0 +1,37 @@ +name: Detect mshta exe running scripts in command-line arguments +id: b89919ed-fe5f-492c-b139-95dqb161039e +version: 2 +date: '2018-12-03' +description: This search looks for the execution of "mshta.exe" with command-line + arguments that launch a script. The search will return the first time and last time + these command-line arguments were used for these executions, as well as the target + system, the user, process "mshta.exe" and its parent process. +how_to_implement: To successfully implement this search, you need to be ingesting + logs with the process name, parent process, and command-line executions from your + endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the + Sysmon TA. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| + search (process=*vbscript* OR process=*javascript*) | `detect_mshta_exe_running_scripts_in_command_line_arguments_filter`' +known_false_positives: Although unlikely, some legitimate applications may exhibit + this behavior, triggering a false positive. +tags: + analytics_story: + - Suspicious MSHTA Activity + mitre_attack_id: + - T1059 + kill_chain_phases: + - Exploitation + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_new_api_calls_from_user_roles.yml b/detections/detect_new_api_calls_from_user_roles.yml new file mode 100644 index 0000000000..ccba53241e --- /dev/null +++ b/detections/detect_new_api_calls_from_user_roles.yml @@ -0,0 +1,36 @@ +name: Detect new API calls from user roles +id: 22773e84-bac0-4595-b086-20d3f335b4f1 +version: 1 +date: '2018-04-16' +description: This search detects new API calls that have either never been seen before + or that have not been seen in the previous hour, where the identity type is `AssumedRole`. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. This search works best when you run the "Previously seen API call per user + roles in CloudTrail" support search once to create a history of previously seen + user roles. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole + [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole + | stats earliest(_time) as earliest latest(_time) as latest by userName eventName + | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) + as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| + eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | + where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` + | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) + as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` + | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter`' +known_false_positives: It is possible that there are legitimate user roles making + new or infrequently used API calls in your infrastructure, causing the search to + trigger. +tags: + analytics_story: + - AWS User Monitoring + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/detect_new_local_admin_account.yml b/detections/detect_new_local_admin_account.yml new file mode 100644 index 0000000000..4d8279cc53 --- /dev/null +++ b/detections/detect_new_local_admin_account.yml @@ -0,0 +1,57 @@ +name: Detect New Local Admin account +id: b25f6f62-0712-43c1-b203-083231ffd97d +version: 1 +date: '2019-02-28' +description: This search looks for newly created accounts that have been elevated + to local administrators. +how_to_implement: 'You must be ingesting Windows Security logs. You must also enable + the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. + Additionally, this search requires you to enable your Group Management Audit Logs + in your Local Windows Security Policy and to be ingesting those logs. More information + on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. + Finally, please make sure that the local administrator group name is "Administrators" + to be able to look for the right group membership changes.\ + + This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not + yet supported by ES Incident Review and therefore cannot be viewed when a notable + event is raised. These fields contribute additional context to the notable. To see + the additional metadata, add the following fields, if not already present, to Incident + Review - Event Attributes (Configure > Incident Management > Incident Review Settings + > Add New Entry):\\n1. **Label:** Security ID, **Field:** Security_ID\ + + 1. \ + + 1. **Label:** Group Name, **Field:** Group_Name\ + + 1. \ + + 1. **Label:** Message, **Field:** Message\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`wineventlog_security` signature_id=4720 OR (signature_id=4732 Group_Name= + Administrators) | transaction Security_ID connected=false maxspan=180m | search + signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID + Group_Name src_user Message | `detect_new_local_admin_account_filter`' +known_false_positives: The activity may be legitimate. For this reason, it's best + to verify the account with an administrator and ask whether there was a valid service + request for the account creation. If your local administrator group name is not + "Administrators", this search may generate an excessive number of false positives +tags: + analytics_story: + - DHS Report TA18-074A + mitre_attack_id: + - T1078 + kill_chain_phases: + - Actions on Objectives + - Command and Control + cis20: + - CIS 16 + nist: + - PR.AC + - DE.CM + security_domain: access + asset_type: Windows diff --git a/detections/detect_new_login_attempts_to_routers.yml b/detections/detect_new_login_attempts_to_routers.yml new file mode 100644 index 0000000000..f1a47e286b --- /dev/null +++ b/detections/detect_new_login_attempts_to_routers.yml @@ -0,0 +1,33 @@ +name: Detect New Login Attempts to Routers +id: 104658f4-afdc-499e-9719-17243rr826f1 +version: 1 +date: '2017-09-12' +description: The search queries the authentication logs for assets that are categorized + as routers in the ES Assets and Identity Framework, to identify connections that + have not been seen before in the last 30 days. +how_to_implement: To successfully implement this search, you must ensure the network + router devices are categorized as "router" in the Assets and identity table. You + must also populate the Authentication data model with logs related to users authenticating + to routing infrastructure. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest + latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router + by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), + "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` + | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`' +known_false_positives: Legitimate router connections may appear as new connections +tags: + analytics_story: + - Router and Infrastructure Security + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 11 + nist: + - PR.PT + - PR.AC + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_new_open_s3_buckets.yml b/detections/detect_new_open_s3_buckets.yml new file mode 100644 index 0000000000..889947bac6 --- /dev/null +++ b/detections/detect_new_open_s3_buckets.yml @@ -0,0 +1,35 @@ +name: Detect New Open S3 buckets +id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4 +version: 1 +date: '2018-07-25' +description: This search looks for CloudTrail events where a user has created an open/public + S3 bucket. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail + inputs. The threshold value should be tuned to your environment. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` AllUsers eventName=PutBucketAcl | spath output=userIdentityArn + path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName + | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList + | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath + input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table + _time, src,awsRegion Permission, Grantee.URI, bucketName, user | `detect_new_open_s3_buckets_filter`' +known_false_positives: While this search has no known false positives, it is possible + that an AWS admin has legitimately created a public bucket for a specific purpose. + That said, AWS strongly advises against granting full control to the "All Users" + group. +tags: + analytics_story: + - Suspicious AWS S3 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - PR.DS + - PR.AC + - DE.CM + security_domain: network + asset_type: S3 Bucket diff --git a/detections/detect_new_user_aws_console_login.yml b/detections/detect_new_user_aws_console_login.yml new file mode 100644 index 0000000000..18071951e4 --- /dev/null +++ b/detections/detect_new_user_aws_console_login.yml @@ -0,0 +1,38 @@ +name: Detect new user AWS Console Login +id: ada0f478-84a8-4641-a3f3-d82362dffd75 +version: 1 +date: '2018-04-30' +description: This search looks for CloudTrail events wherein a console login event + by a user was recorded within the last hour, then compares the event to a lookup + file of previously seen users (by ARN values) who have logged into the console. + The alert is fired if the user has logged into the console for the first time within + the last hour +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the "Previously seen users in CloudTrail" support search only once to + create a baseline of previously seen IAM users within the last 30 days. Run "Update + previously seen users in CloudTrail" hourly (or more frequently depending on how + often you run the detection searches) to refresh the baselines. +type: ESCU +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats + earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t + previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) + as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), + "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| + where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter`' +known_false_positives: When a legitimate new user logins for the first time, this + activity will be detected. Check how old the account is and verify that the user + activity is legitimate. +tags: + analytics_story: + - Suspicious AWS Login Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_oulook_exe_writing_a__zip_file.yml b/detections/detect_oulook_exe_writing_a__zip_file.yml new file mode 100644 index 0000000000..e5d0e39b9f --- /dev/null +++ b/detections/detect_oulook_exe_writing_a__zip_file.yml @@ -0,0 +1,46 @@ +name: Detect Oulook exe writing a zip file +id: a51bfe1a-94f0-4822-b1e4-16ae10145893 +version: 2 +date: '2020-03-16' +description: This search looks for execution of process `outlook.exe` where the process + is writing a `.zip` file to the disk. +how_to_implement: You must be ingesting data that records filesystem and process activity + from your hosts to populate the Endpoint data model. This is typically populated + via endpoint detection-and-response products, such as Carbon Black, or endpoint + data sources, such as Sysmon. +type: ESCU +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe + OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id + Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name + Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename + parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` + count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as + file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR + Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) + by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | + `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path + file_name file_hash count file_id] | table firstTime lastTime user malicious_id + outlook_id process_name parent_process_name file_name file_path | where file_name + != "" | `detect_oulook_exe_writing_a__zip_file_filter` ' +known_false_positives: It is not uncommon for outlook to write legitimate zip files + to the disk. +tags: + analytics_story: + - Phishing Payloads + mitre_attack_id: + - T1193 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 7 + - CIS 8 + nist: + - ID.AM + - PR.DS + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_outbound_smb_traffic.yml b/detections/detect_outbound_smb_traffic.yml new file mode 100644 index 0000000000..6e20f9fd16 --- /dev/null +++ b/detections/detect_outbound_smb_traffic.yml @@ -0,0 +1,50 @@ +name: Detect Outbound SMB Traffic +id: 7f5fb3e1-4209-414-90db-0ec21b936378 +version: 2 +date: '2020-01-22' +description: This search looks for outbound SMB connections made by hosts within your + network to the Internet. SMB traffic is used for Windows file-sharing activity. + One of the techniques often used by attackers involves retrieving the credential + hash using an SMB request made to a compromised server controlled by the threat + actor. +how_to_implement: 'In order to run this search effectively, we highly recommend that + you leverage the Assets and Identity framework. It is important that you have good + understanding of how your network segments are designed, and be able to distinguish + internal from external address space. Add a category named `internal` to the CIDRs + that host the company''s assets in `assets_by_cidr.csv` lookup file, which is located + in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating + this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. + This search also requires you to be ingesting your network traffic and populating + the Network_Traffic data model' +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest + latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic + where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 + OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip + | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 + AND dest_ip!=192.168.0.0/16) | `security_content_ctime(earliest)`| `security_content_ctime(latest)` + | `detect_outbound_smb_traffic_filter` ' +known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic + is legitimate, if the company's internal networks are not well-defined in the Assets + and Identity Framework. Categorize the internal CIDR blocks as `internal` in the + lookup file to avoid creating notable events for traffic destined to those CIDR + blocks. Any other network connection that is going out to the Internet should be + investigated and blocked. Best practices suggest preventing external communications + of all SMB versions and related protocols at the network boundary. +tags: + analytics_story: + - Hidden Cobra Malware + - DHS Report TA18-074A + mitre_attack_id: + - T1043 + kill_chain_phases: + - Actions on Objectives + - Command and Control + cis20: + - CIS 12 + nist: + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/detect_path_interception_by_creation_of_program_exe.yml b/detections/detect_path_interception_by_creation_of_program_exe.yml new file mode 100644 index 0000000000..39dcbe18b9 --- /dev/null +++ b/detections/detect_path_interception_by_creation_of_program_exe.yml @@ -0,0 +1,34 @@ +name: Detect Path Interception By Creation Of program exe +id: c77162d3-f93c-45cc-80c8-22f6v5264g9f +version: 2 +date: '2018-11-15' +description: 'The search is looking for the creation of program.exe in the C: drive. The + creation of this file in that location may be driven by a motive to perform path + interception.' +how_to_implement: You must be ingesting data that records the file-system activity + from your hosts to populate the Endpoint file-system data model node. This is typically + populated via endpoint detection-and-response products, such as Carbon Black, or + other endpoint data sources, such as Sysmon. The data used for this search is typically + generated via logs that report file system reads and writes. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_hash) + as file_hash values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem + where Filesystem.file_path="C:\\program.exe" by Filesystem.file_name | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `detect_path_interception_by_creation_of_program_exe_filter`' +known_false_positives: 'It is unlikely that a normal user may create and place this + file in the C: drive. Confirm with the user.' +tags: + analytics_story: + - Windows Persistence Techniques + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/detect_processes_used_for_system_network_configuration_discovery.yml b/detections/detect_processes_used_for_system_network_configuration_discovery.yml new file mode 100644 index 0000000000..10b955b026 --- /dev/null +++ b/detections/detect_processes_used_for_system_network_configuration_discovery.yml @@ -0,0 +1,39 @@ +name: Detect processes used for System Network Configuration Discovery +id: a51bfe1a-94f0-48cc-b1e4-16ae10145893 +version: 1 +date: '2018-11-20' +description: This search looks for fast execution of processes used for system network + configuration discovery on the endpoint. +how_to_implement: You must be ingesting data that records registry activity from your + hosts to populate the Endpoint data model in the processes node. This is typically + populated via endpoint detection-and-response products, such as Carbon Black, or + endpoint data sources, such as Sysmon. The data used for this search is usually + generated via logs that report reads and writes to the registry or that are populated + via Windows event logs, after enabling process tracking in your Windows audit settings. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name + Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` + | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime + lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter`' +known_false_positives: It is uncommon for normal users to execute a series of commands + used for network discovery. System administrators often use scripts to execute these + commands. These can generate false positives. +tags: + analytics_story: + - Unusual Processes + kill_chain_phases: + - Installation + - Command and Control + - Actions on Objectives + cis20: + - CIS 2 + nist: + - ID.AM + - PR.DS + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/detect_prohibited_applications_spawning_cmd_exe.yml new file mode 100644 index 0000000000..94a4c9cb39 --- /dev/null +++ b/detections/detect_prohibited_applications_spawning_cmd_exe.yml @@ -0,0 +1,37 @@ +name: Detect Prohibited Applications Spawning cmd exe +id: dcfd6b40-42f9-469d-a433-2e53f7486664 +version: 3 +date: '2020-02-03' +description: This search looks for executions of cmd.exe spawned by a process that + is often abused by attackers and that does not typically launch cmd.exe. +how_to_implement: You must be ingesting data that records process activity from your + hosts and populates the Endpoint data model with the resultant dataset. This search + includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list + of processes that should not be spawning cmd.exe. You can modify this lookup to + better suit your environment. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name + Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter`' +known_false_positives: There are circumstances where an application may legitimately + execute and interact with the Windows command-line interface. Investigate and modify + the lookup file, as appropriate. +tags: + analytics_story: + - Suspicious Command-Line Executions + - Suspicious MSHTA Activity + mitre_attack_id: + - T1059 + kill_chain_phases: + - Exploitation + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_psexec_with_accepteula_flag.yml b/detections/detect_psexec_with_accepteula_flag.yml new file mode 100644 index 0000000000..8e9cda022b --- /dev/null +++ b/detections/detect_psexec_with_accepteula_flag.yml @@ -0,0 +1,44 @@ +name: Detect PsExec With accepteula Flag +id: b89919ed-fe5f-492c-b139-151xb162040e +version: 2 +date: '2019-02-26' +description: This search looks for events where `PsExec.exe` is run with the `accepteula` + flag in the command line. PsExec is a built-in Windows utility that enables you + to execute processes on other systems. It is fully interactive for console applications. + This tool is widely used for launching interactive command prompts on remote systems. + Threat actors leverage this extensively for executing code on compromised systems. + If an attacker is running PsExec for the first time, they will be prompted to accept + the end-user license agreement (EULA), which can be passed as the argument `accepteula` + within the command line. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = PsExec.exe Processes.process = "*accepteula*" by + Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| + `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`' +known_false_positives: Administrators can leverage PsExec for accessing remote systems + and might pass `accepteula` as an argument if they are running this tool for the + first time. However, it is not likely that you'd see multiple occurrences of this + event on a machine +tags: + analytics_story: + - SamSam Ransomware + - DHS Report TA18-074A + mitre_attack_id: + - T1059 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_rare_executables.yml b/detections/detect_rare_executables.yml new file mode 100644 index 0000000000..4594d7567d --- /dev/null +++ b/detections/detect_rare_executables.yml @@ -0,0 +1,46 @@ +name: Detect Rare Executables +id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac +version: 5 +date: '2020-03-16' +description: This search will return a table of rare processes, the names of the systems + running them, and the users who initiated each process. +how_to_implement: To successfully implement this search, you must be ingesting data + that records process activity from your hosts and populating the endpoint data model + with the resultant dataset. The macro `filter_rare_process_whitelist` searches two + lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` + and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, + add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from + the default lookup file, you will have to modify the macro itself to set the whitelist + value for that process to false. You can modify the limit parameter and search scheduling + to better suit your environment. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.dest) as + dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime + from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name + as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes + by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name + as process| `filter_rare_process_whitelist`| table process ] | `detect_rare_executables_filter` ' +known_false_positives: Some legitimate processes may be only rarely executed in your + environment. As these are identified, update `rare_process_whitelist_local.csv` + to filter them out of your search results. +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Unusual Processes + kill_chain_phases: + - Installation + - Command and Control + - Actions on Objectives + cis20: + - CIS 2 + - CIS 8 + nist: + - ID.AM + - PR.PT + - PR.DS + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_s3_access_from_a_new_ip.yml b/detections/detect_s3_access_from_a_new_ip.yml new file mode 100644 index 0000000000..0230e522c9 --- /dev/null +++ b/detections/detect_s3_access_from_a_new_ip.yml @@ -0,0 +1,39 @@ +name: Detect S3 access from a new IP +id: 2a9b80d3-6340-4345-b5ad-291bq3d0daq4 +version: 1 +date: '2018-06-28' +description: This search looks at S3 bucket-access logs and detects new or previously + unseen remote IP addresses that have successfully accessed an S3 bucket. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access + logs' inputs. This search works best when you run the "Previously Seen S3 Bucket + Access by Remote IP" support search once to create a history of previously seen + remote IPs and bucket names. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 + | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip + | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) + as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup + previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), + "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip + | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`' +known_false_positives: S3 buckets can be accessed from any IP, as long as it can make + a successful connection. This will be a false postive, since the search is looking + for a new IP within the past hour +tags: + analytics_story: + - Suspicious AWS S3 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + - CIS 14 + nist: + - PR.DS + - PR.AC + - DE.CM + security_domain: network + asset_type: S3 Bucket diff --git a/detections/detect_spike_in_aws_api_activity.yml b/detections/detect_spike_in_aws_api_activity.yml new file mode 100644 index 0000000000..297e860092 --- /dev/null +++ b/detections/detect_spike_in_aws_api_activity.yml @@ -0,0 +1,64 @@ +name: Detect Spike in AWS API Activity +id: ada0f478-84a8-4641-a3f1-d32362d4bd55 +version: 1 +date: '2018-04-09' +description: This search will detect users creating spikes of API activity in your + AWS environment. It will also update the cache file that factors in the latest + data. +how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit + your environment. The `dataPointThreshold` variable is the minimum number of data + points required to have a statistically significant amount of data to determine. + The `deviationThreshold` variable is the number of standard deviations away from + the mean that the value must be to be considered a spike.\ + + This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) + that are not yet supported by ES Incident Review and therefore cannot be viewed + when a notable event is raised. These fields contribute additional context to the + notable. To see the additional metadata, add the following fields, if not already + present, to Incident Review - Event Attributes (Configure > Incident Management + > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** + eventName\ + + 1. \ + + 1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ + + 1. \ + + 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall + | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup + api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * + by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 + | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) + | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, + stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) + | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup + api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 + | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND + numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn + | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) + as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user + | `detect_spike_in_aws_api_activity_filter`' +known_false_positives: '' +tags: + analytics_story: + - AWS User Monitoring + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.CM + - PR.AC + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml b/detections/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml new file mode 100644 index 0000000000..fcdd2fdaa9 --- /dev/null +++ b/detections/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml @@ -0,0 +1,57 @@ +name: Detect Spike in blocked Outbound Traffic from your AWS +id: ada0f278-84a8-46w1-a3f1-w32372d4bd53 +version: 1 +date: '2018-05-07' +description: This search will detect spike in blocked outbound network connections + originating from within your AWS environment. It will also update the cache file + that factors in the latest data. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow + logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit + your environment. The `dataPointThreshold` variable is the number of data points + required to meet the definition of "spike." The `deviationThreshold` variable is + the number of standard deviations away from the mean that the value must be to be + considered a spike. This search works best when you run the "Baseline of Blocked + Outbound Connection" support search once to create a history of previously seen + blocked outbound connections. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 + OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` + action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) + ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | + stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections + append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections + as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 + | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) + | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), + stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), + numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, + latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup + baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold + = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) + AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] + | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" + count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip + | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`' +known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` + and `deviationThreshold`. Additionally, false positives may result when AWS administrators + roll out policies enforcing network blocks, causing sudden increases in the number + of blocked outbound connections. +tags: + analytics_story: + - AWS Network ACL Activity + - Suspicious AWS Traffic + - Command and Control + kill_chain_phases: + - Actions on Objectives + - Command and Control + cis20: + - CIS 11 + nist: + - DE.AE + - DE.CM + - PR.AC + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_spike_in_network_acl_activity.yml b/detections/detect_spike_in_network_acl_activity.yml new file mode 100644 index 0000000000..b4d7dcb368 --- /dev/null +++ b/detections/detect_spike_in_network_acl_activity.yml @@ -0,0 +1,49 @@ +name: Detect Spike in Network ACL Activity +id: ada0f478-84a8-4641-a1f1-e32372d4bd53 +version: 1 +date: '2018-05-21' +description: This search will detect users creating spikes in API activity related + to network access-control lists (ACLs)in your AWS environment. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit + your environment. The `dataPointThreshold` variable is the minimum number of data + points required to have a statistically significant amount of data to determine. + The `deviationThreshold` variable is the number of standard deviations away from + the mean that the value must be to be considered a spike. This search works best + when you run the "Baseline of Network ACL Activity by ARN" support search once to + create a lookup file of previously seen Network ACL Activity. To add or remove API + event names related to network ACLs, edit the macro `network_acl_events`. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` + | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup + network_acl_activity_baseline append=t | fields - latestCount | stats values(*) + as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, + 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, + avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), + numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, + stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold + = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) + AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as + userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn + | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) + as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter`' +known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` + and `deviationThreshold`. Please modify this according the your environment. +tags: + analytics_story: + - AWS Network ACL Activity + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 12 + - CIS 11 + nist: + - DE.DP + - DE.CM + - PR.AC + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_spike_in_s3_bucket_deletion.yml b/detections/detect_spike_in_s3_bucket_deletion.yml new file mode 100644 index 0000000000..ddfd7e1b94 --- /dev/null +++ b/detections/detect_spike_in_s3_bucket_deletion.yml @@ -0,0 +1,48 @@ +name: Detect Spike in S3 Bucket deletion +id: ad12w478-84a8-4641-a3w1-e32372q4bd53 +version: 1 +date: '2018-11-27' +description: This search detects users creating spikes in API activity related to + deletion of S3 buckets in your AWS environment. It will also update the cache file + that factors in the latest data. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit + your environment. The `dataPointThreshold` variable is the minimum number of data + points required to have a statistically significant amount of data to determine. + The `deviationThreshold` variable is the number of standard deviations away from + the mean that the value must be to be considered a spike. This search works best + when you run the "Baseline of S3 Bucket deletion activity by ARN" support search + once to create a baseline of previously seen S3 bucket-deletion activity. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket + | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup + s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn + | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 + | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) + | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, + stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) + | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup + s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval + isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints + > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | + table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName + path=requestParameters.bucketName | stats values(bucketName) as bucketName, count + as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`' +known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, + the false positive rate may vary. Please modify this according the your environment. +tags: + analytics_story: + - Suspicious AWS S3 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 13 + nist: + - DE.DP + - DE.CM + - PR.AC + security_domain: network + asset_type: S3 Bucket diff --git a/detections/detect_spike_in_security_group_activity.yml b/detections/detect_spike_in_security_group_activity.yml new file mode 100644 index 0000000000..92c2cc49ae --- /dev/null +++ b/detections/detect_spike_in_security_group_activity.yml @@ -0,0 +1,49 @@ +name: Detect Spike in Security Group Activity +id: ada0f478-84a8-4641-a3f1-e32372d4bd53 +version: 1 +date: '2018-04-18' +description: This search will detect users creating spikes in API activity related + to security groups in your AWS environment. It will also update the cache file + that factors in the latest data. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit + your environment. The `dataPointThreshold` variable is the minimum number of data + points required to have a statistically significant amount of data to determine. + The `deviationThreshold` variable is the number of standard deviations away from + the mean that the value must be to be considered a spike.This search works best + when you run the "Baseline of Security Group Activity by ARN" support search once + to create a history of previously seen Security Group Activity. To add or remove + API event names for security groups, edit the macro `security_group_api_calls`. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` + | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup + security_group_activity_baseline append=t | fields - latestCount | stats values(*) + as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, + 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, + avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), + numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, + stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold + = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) + AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as + userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn + | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) + as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter`' +known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, + the false positive rate may vary. Please modify this according the your environment. +tags: + analytics_story: + - AWS User Monitoring + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.DP + - DE.CM + - PR.AC + security_domain: network + asset_type: AWS Instance diff --git a/detections/detect_unauthorized_assets_by_mac_address.yml b/detections/detect_unauthorized_assets_by_mac_address.yml new file mode 100644 index 0000000000..252c9490af --- /dev/null +++ b/detections/detect_unauthorized_assets_by_mac_address.yml @@ -0,0 +1,42 @@ +name: Detect Unauthorized Assets by MAC address +id: dcfd6b40-42f9-469d-a433-2e53f7489ff4 +version: 1 +date: '2017-09-13' +description: By populating the organization's assets within the assets_by_str.csv, + we will be able to detect unauthorized devices that are trying to connect with the + organization's network by inspecting DHCP request packets, which are issued by devices + when they attempt to obtain an IP address from the DHCP server. The MAC address + associated with the source of the DHCP request is checked against the list of known + devices, and reports on those that are not found. +how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise + Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv + file located in SA-IdentityManagement, which will contain a list of known authorized + organizational assets including their MAC addresses. Ensure that all inventoried + systems have their MAC address populated. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions + where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip + All_Sessions.src_mac | dedup All_Sessions.src_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` + | search NOT [| inputlookup asset_lookup_by_str |rename mac as src_mac | fields + + src_mac] | `detect_unauthorized_assets_by_mac_address_filter`' +known_false_positives: This search might be prone to high false positives. Please + consider this when conducting analysis or investigations. Authorized devices may + be detected as unauthorized. If this is the case, verify the MAC address of the + system responsible for the false positive and add it to the Assets and Identity + framework with the proper information. +tags: + analytics_story: + - Asset Tracking + kill_chain_phases: + - Reconnaissance + - Delivery + - Actions on Objectives + cis20: + - CIS 1 + nist: + - ID.AM + - PR.DS + security_domain: network + asset_type: Infrastructure diff --git a/detections/detect_usb_device_insertion.yml b/detections/detect_usb_device_insertion.yml new file mode 100644 index 0000000000..f5da48a4e2 --- /dev/null +++ b/detections/detect_usb_device_insertion.yml @@ -0,0 +1,39 @@ +name: Detect USB device insertion +id: 104658f4-afdc-499f-9719-17a43f9826f5 +version: 1 +date: '2017-11-27' +description: The search is used to detect hosts that generate Windows Event ID 4663 + for successful attempts to write to or read from a removable storage and Event ID + 4656 for failures, which occurs when a USB drive is plugged in. In this scenario + we are querying the Change_Analysis data model to look for Windows Event ID 4656 + or 4663 where the priority of the affected host is marked as high in the ES Assets + and Identity Framework. +how_to_implement: To successfully implement this search, you must ingest Windows Security + Event logs and track event code 4663 and 4656. Ensure that the field from the event + logs is being mapped to the result_id field in the Change_Analysis data model. To + minimize the alert volume, this search leverages the Assets and Identity framework + to filter out events from those assets not marked high priority in the Enterprise + Security Assets and Identity Framework. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count earliest(_time) AS earliest + latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) + All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) + (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| + `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter`' +known_false_positives: Legitimate USB activity will also be detected. Please verify + and investigate as appropriate. +tags: + analytics_story: + - Data Protection + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 13 + nist: + - PR.PT + - PR.DS + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_use_of_cmd_exe_to_launch_script_interpreters.yml b/detections/detect_use_of_cmd_exe_to_launch_script_interpreters.yml new file mode 100644 index 0000000000..d261e24136 --- /dev/null +++ b/detections/detect_use_of_cmd_exe_to_launch_script_interpreters.yml @@ -0,0 +1,37 @@ +name: Detect Use of cmd exe to Launch Script Interpreters +id: b89919ed-fe5f-492c-b139-95dbb162039e +version: 3 +date: '2020-03-02' +description: This search looks for the execution of the cscript.exe or wscript.exe + processes, with a parent of cmd.exe. The search will return the count, the first + and last time this execution was seen on a machine, the user, and the destination + of the machine +how_to_implement: To successfully implement this search, you must be ingesting data + that records process activity from your hosts to populate the endpoint data model + in the processes node. If you are using Sysmon, you must have at least version 6.0.4 + of the Sysmon TA. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe + OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name + Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`' +known_false_positives: Some legitimate applications may exhibit this behavior. +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Command-Line Executions + mitre_attack_id: + - T1059 + kill_chain_phases: + - Exploitation + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/detect_web_traffic_to_dynamic_domain_providers.yml b/detections/detect_web_traffic_to_dynamic_domain_providers.yml new file mode 100644 index 0000000000..45b21f753b --- /dev/null +++ b/detections/detect_web_traffic_to_dynamic_domain_providers.yml @@ -0,0 +1,48 @@ +name: Detect web traffic to dynamic domain providers +id: 134da869-e264-4a8f-8d7e-fcd01c18f301 +version: 1 +date: '2018-09-06' +description: This search looks for web connections to dynamic DNS providers. +how_to_implement: 'This search requires you to be ingesting web-traffic logs. You + can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis + tool, such as Bro or Splunk Stream. The web data model must contain the URL being + requested, the IP address of the host initiating the request, and the destination + IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, + which contains a non-exhaustive list of dynamic DNS providers. Consider periodically + updating this local lookup file with new domains.\ + + This search produces fields (`isDynDNS`) that are not yet supported by ES Incident + Review and therefore cannot be viewed when a notable event is raised. These fields + contribute additional context to the notable. To see the additional metadata, add + the following fields, if not already present, to Incident Review - Event Attributes + (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. + **Label:** IsDynamicDNS, **Field:** isDynDNS\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Web.url) as url min(_time) + as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status + | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` + | `detect_web_traffic_to_dynamic_domain_providers_filter`' +known_false_positives: It is possible that list of dynamic DNS providers is outdated + and/or that the URL being requested is legitimate. +tags: + analytics_story: + - Dynamic DNS + mitre_attack_id: + - T1102 + - T1041 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 7 + - CIS 8 + nist: + - PR.IP + - DE.DP + security_domain: network + asset_type: Endpoint diff --git a/detections/detection_of_dns_tunnels.yml b/detections/detection_of_dns_tunnels.yml new file mode 100644 index 0000000000..2027a04314 --- /dev/null +++ b/detections/detection_of_dns_tunnels.yml @@ -0,0 +1,56 @@ +name: Detection of DNS Tunnels +id: 104658f4-afdc-499f-9719-17a43f9826f4 +version: 1 +date: '2017-09-18' +description: This search is used to detect DNS tunneling, by calculating the sum of + the length of DNS queries and DNS answers. The search also filters out potential + false positives by filtering out queries made to internal systems and the queries + originating from internal DNS, Web, and Email servers. Endpoints using DNS as a + method of transmission for data exfiltration, command and control, or evasion of + security controls can often be detected by noting an unusually large volume of DNS + traffic. +how_to_implement: To successfully implement this search, we must ensure that DNS data + is being ingested and mapped to the appropriate fields in the Network_Resolution + data model. Fields like src_category are automatically provided by the Assets and + Identity Framework shipped with Splunk Enterprise Security. You will need to ensure + you are using the Assets and Identity Framework and populating the src_category + field. You will also need to enable the `cim_corporate_web_domain_search()` macro + which will essentially filter out the DNS queries made to the corporate web domains + to reduce alert fatigue. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` dc("DNS.query") as count from + datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT + (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" + NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" + OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" + as src "DNS.query" as message | eval length=len(message) | stats sum(length) as + length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") + as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" + NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" + NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" + OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename + "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", + message) | eval length=len(message) | stats sum(length) as length by src ] | stats + sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter`' +known_false_positives: It's possible that normal DNS traffic will exhibit this behavior. + If an alert is generated, please investigate and validate as appropriate. The threshold + can also be modified to better suit your environment. +tags: + analytics_story: + - Data Protection + - Suspicious DNS Traffic + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 13 + nist: + - PR.PT + - PR.DS + security_domain: network + asset_type: Endpoint diff --git a/detections/detection_of_tools_built_by_nirsoft.yml b/detections/detection_of_tools_built_by_nirsoft.yml new file mode 100644 index 0000000000..64ad0bd294 --- /dev/null +++ b/detections/detection_of_tools_built_by_nirsoft.yml @@ -0,0 +1,36 @@ +name: Detection of tools built by NirSoft +id: 1297fb80-f42a-4q4a-9c8b-78c061417cf6 +version: 2 +date: '2018-12-03' +description: This search looks for specific command-line arguments that may indicate + the execution of tools made by Nirsoft, which are legitimate, but may be abused + by attackers. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) + as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* + /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name + Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter`' +known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You + should verfiy that the tool was used for a legitimate purpose. +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + mitre_attack_id: + - T1072 + - T1087 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 3 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/disable_remote_uac.yml b/detections/disable_remote_uac.yml deleted file mode 100644 index 0395ab920e..0000000000 --- a/detections/disable_remote_uac.yml +++ /dev/null @@ -1,106 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-10-12' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The search looks for modifications to registry keys that control the - enforcement of Windows User Account Control (UAC). -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, registry_path - rule_description: The registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy - was modified. This registry key is associated with disabling remote UAC - on Windows. - rule_title: Registry Key Associated With Disabling Remote UAC Modified on - $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*Windows\\CurrentVersion\\Policies\\System\\LocalAccountTokenFilterPolicy" - by Registry.dest, Registry.registry_key_name Registry.user - Registry.registry_path Registry.action | `drop_dm_object_name(Registry)`' - suppress: - suppress_fields: dest, user, registry_path - suppress_period: 14400s -eli5: This search checks to see if the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy - was modified. This registry key can be used to disable remote User Account Control. The - search returns the count, the first time activity was seen, last time activity was - seen, the registry path that was modified, the host where the modification took - place and the user that performed the modification. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report registry - modifications. -id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: This registry key may be modified via administrators to implement - a change in system policy. This type of change should be a very rare occurrence. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Modify Registry - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-02' -name: Disabling Remote User Account Control -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/disabling_remote_user_account_control.yml b/detections/disabling_remote_user_account_control.yml new file mode 100644 index 0000000000..ecb9b0b969 --- /dev/null +++ b/detections/disabling_remote_user_account_control.yml @@ -0,0 +1,36 @@ +name: Disabling Remote User Account Control +id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c +version: 3 +date: '2020-03-02' +description: The search looks for modifications to registry keys that control the + enforcement of Windows User Account Control (UAC). +how_to_implement: To successfully implement this search, you must be ingesting data + that records registry activity from your hosts to populate the endpoint data model + in the registry node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. + The data used for this search is typically generated via logs that report registry + modifications. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*Windows\\CurrentVersion\\Policies\\System\\LocalAccountTokenFilterPolicy" + by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path + Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`' +known_false_positives: This registry key may be modified via administrators to implement + a change in system policy. This type of change should be a very rare occurrence. +tags: + analytics_story: + - Windows Defense Evasion Tactics + - Suspicious Windows Registry Activities + mitre_attack_id: + - T1112 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/dns_amplification_any_query.yml b/detections/dns_amplification_any_query.yml deleted file mode 100644 index 0f1d9d1698..0000000000 --- a/detections/dns_amplification_any_query.yml +++ /dev/null @@ -1,87 +0,0 @@ -asset_type: DNS Servers -confidence: high -creation_date: '2016-08-24' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: The search is used to identify attempts to use your DNS Infrastructure - for DDoS purposes via a DNS amplification attack leveraging ANY queries. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The search is used to identify attempts to use your DNS - Infrastructure for DDoS purposes via a DNS amplification attack leveraging - ANY queries. - rule_title: Large Volume of DNS ANY Queries - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: '*/5 * * * *' - earliest_time: -15m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where - nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" - | `drop_dm_object_name("DNS")` | where count>200' - suppress: - suppress_fields: dest - suppress_period: 7200s -eli5: This search counts the number of DNS ANY queries received in 5 minutes, and - generates a Notable Event if the count exceeds a predefined threshold. The search - returns the count, the first time, and the last time a DNS packet was observed with - the ANY flag set. -entities: - - dest -how_to_implement: To successfully implement this search you must ensure that DNS data - is populating the Network_Resolution data model. -id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk -known_false_positives: Legitimate ANY requests may trigger this search, however it - is unusual to see a large volume of them under typical circumstances. You may modify - the threshold in the search to better suit your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 11 - - CIS 12 - kill_chain_phases: - - Actions on Objectives - nist: - - PR.PT - - DE.AE - - PR.IP -modification_date: '2017-09-20' -name: Large Volume of DNS ANY Queries -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/dns_evilginx_subdomains.yml b/detections/dns_evilginx_subdomains.yml deleted file mode 100644 index 45f4a674b4..0000000000 --- a/detections/dns_evilginx_subdomains.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2019-04-29' -data_metadata: - data_models: - - Network_Resolution - - Web - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search looks for DNS requests for phishing domains that are leveraging - EvilGinx tools to mimic websites. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, query - rule_description: The host $src$ issued a DNS request for a domain that could - be a phishing site leverating EvilGinx toolkit. - rule_title: DNS request for EvilGinx subdomain detected on $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) - as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS - by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query - ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) - as query by domain dest src answer| search `evilginx_phishlets_amazon` OR - `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` - OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` - | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain - type=outer [| tstats count `security_content_summariesonly` values(Web.url) - as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | - rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table - dest domain url] | table count src dest query answer domain url' - suppress: - suppress_fields: src, query - suppress_period: 14400s -eli5: This search gathers all the answers to each system's DNS query, then filters - for queries that have subdomains extracted from the EvilGinx toolkit. It will then - run a regex to extract `legit_domains` from the query and remove that from the detection - if it is listed in the `legit_domains.csv` -entities: - - src -how_to_implement: "You need to ingest data from your DNS logs in the Network_Resolution\ - \ datamodel. Specifically you must ingest the domain that is being queried and the\ - \ IP of the host originating the request. Ideally, you should also be ingesting\ - \ the answer to the query and the query type. This approach allows you to also create\ - \ your own localized passive DNS capability which can aid you in future investigations.\ - \ You will have to add legitimate domain names to the `legit_domains.csv` file shipped\ - \ with the app. \\\n **Splunk>Phantom Playbook Integration**\\\nIf Splunk>Phantom\ - \ is also configured in your environment, a Playbook called `Lets Encrypt Domain\ - \ Investigate` can be configured to run when any results are found by this detection\ - \ search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,\ - \ add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response\ - \ Actions when configuring this detection search, and set the corresponding Playbook\ - \ to active. \\\n(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\\\ - \n" -id: 24dd17b1-e2fb-4c31-878c-d4f226595bfa -investigations: - - id: bc91a8cf-35e7-4bb2-2240-e756cc06fd73 - name: Get Certificate logs for a domain - type: splunk - - id: c096f721-8842-42ce-2fc7-742d8272b712 - name: Domain Certificate Investigation - type: phantom -known_false_positives: If a known good domain is not listed in the legit_domains.csv - file, then the search could give you false postives. Please update that lookup file - to filter out DNS requests to legitimate domains. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - - CIS 7 - kill_chain_phases: - - Delivery - - Command and Control - mitre_attack: - - Spearphishing Link - - Command and Control - nist: - - ID.AM - - PR.DS - - PR.IP - - DE.AE - - DE.CM -modification_date: '2019-04-29' -name: Detect DNS requests to Phishing Sites leveraging EvilGinx2 -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/dns_query_length_mltk.yml b/detections/dns_query_length_mltk.yml deleted file mode 100644 index 84914faa68..0000000000 --- a/detections/dns_query_length_mltk.yml +++ /dev/null @@ -1,151 +0,0 @@ -asset_type: Endpoint -baselines: - - id: c914844c-0ff5-4efc-8d44-c063443129ba - name: Baseline of DNS Query Length - MLTK - type: splunk -confidence: medium -creation_date: '2019-05-08' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search allows you to identify DNS requests that are unusually large - for the record type being requested in your environment. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src - rule_description: Identify DNS traffic with unusual query lengths by record - type - rule_title: DNS query length outliers - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) - as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution - by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) - | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" - as isOutlier | search isOutlier > 0 | sort -query_length | table start_time - end_time query record_type count src dest query_length | `dns_query_length_mltk_filter` ' - suppress: - suppress_fields: query - suppress_period: 43200s -eli5: Attackers often use random, long domain names for components of their attack - infrastructure. This search leverages the probability distribution function algorithm - provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length - of the DNS query for each record type observed. The companion search "Baseline of - DNS Query Length - MLTK" creates a machine-learning (ML) model built over the historical - data used by this search. The determination of what is considered an outlier may - be adjusted via the threshold parameter in the search. More information on the algorithm - used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -entities: - - src -how_to_implement: 'To successfully implement this search, you will need to ensure - that DNS data is populating the Network_Resolution data model. In addition, the - Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your - search heads, along with any required dependencies. Finally, the support search - "Baseline of DNS Query Length - MLTK" must be executed before this detection search, - because it builds a machine-learning (ML) model over the historical data used by - this search. It is important that this search is run in the same app context as - the associated support search, so that the model created by the support search is - available for use. You should periodically re-run the support search to rebuild - the model with the latest data available in your environment.\ - - This search produces fields (`query`,`query_length`,`count`) that are not yet supported - by ES Incident Review and therefore cannot be viewed when a notable event is raised. - These fields contribute additional context to the notable. To see the additional - metadata, add the following fields, if not already present, to Incident Review - - Event Attributes (Configure > Incident Management > Incident Review Settings > Add - New Entry):\\n1. **Label:** DNS Query, **Field:** query\ - - 1. \ - - 1. **Label:** DNS Query Length, **Field:** query_length\ - - 1. \ - - 1. **Label:** Number of events, **Field:** count\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: If you are seeing more results than desired, you may consider - reducing the value for threshold in the search. You should also periodically re-run - the support search to re-build the ML model on the latest data. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Exfiltration - - Commonly Used Port - mitre_technique_id: - - T1071 - nist: - - PR.PT - - DE.AE - - DE.CM -modification_date: '2020-01-22' -name: DNS Query Length Outliers - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/dns_query_length_outliers___mltk.yml b/detections/dns_query_length_outliers___mltk.yml new file mode 100644 index 0000000000..6f1bab7aa8 --- /dev/null +++ b/detections/dns_query_length_outliers___mltk.yml @@ -0,0 +1,65 @@ +name: DNS Query Length Outliers - MLTK +id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 +version: 2 +date: '2020-01-22' +description: This search allows you to identify DNS requests that are unusually large + for the record type being requested in your environment. +how_to_implement: 'To successfully implement this search, you will need to ensure + that DNS data is populating the Network_Resolution data model. In addition, the + Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your + search heads, along with any required dependencies. Finally, the support search + "Baseline of DNS Query Length - MLTK" must be executed before this detection search, + because it builds a machine-learning (ML) model over the historical data used by + this search. It is important that this search is run in the same app context as + the associated support search, so that the model created by the support search is + available for use. You should periodically re-run the support search to rebuild + the model with the latest data available in your environment.\ + + This search produces fields (`query`,`query_length`,`count`) that are not yet supported + by ES Incident Review and therefore cannot be viewed when a notable event is raised. + These fields contribute additional context to the notable. To see the additional + metadata, add the following fields, if not already present, to Incident Review - + Event Attributes (Configure > Incident Management > Incident Review Settings > Add + New Entry):\\n1. **Label:** DNS Query, **Field:** query\ + + 1. \ + + 1. **Label:** DNS Query Length, **Field:** query_length\ + + 1. \ + + 1. **Label:** Number of events, **Field:** count\ + + Detailed documentation on how to create a new field within Incident Review may be + found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as start_time + max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution + by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval + query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" + as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time + query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` ' +known_false_positives: If you are seeing more results than desired, you may consider + reducing the value for threshold in the search. You should also periodically re-run + the support search to re-build the ML model on the latest data. +tags: + analytics_story: + - Hidden Cobra Malware + - Suspicious DNS Traffic + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + cis20: + - CIS 8 + - CIS 12 + nist: + - PR.PT + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/dns_query_length_with_high_standard_deviation.yml b/detections/dns_query_length_with_high_standard_deviation.yml index 551e5c046b..1d658defc6 100644 --- a/detections/dns_query_length_with_high_standard_deviation.yml +++ b/detections/dns_query_length_with_high_standard_deviation.yml @@ -1,114 +1,36 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: DNS Query Length With High Standard Deviation +id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 +version: 1 +date: '2020-01-22' description: This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src - rule_description: Filter DNS requests and compute the standard deviation then - filter on 2 times the standard deviation - rule_title: DNS query length with high standard deviation - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by - DNS.query DNS.record_type | `drop_dm_object_name("DNS")` | eval query_length - = len(query) | table query query_length record_type count | eventstats stdev(query_length) - AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) - | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` ' - suppress: - suppress_fields: query - suppress_period: 43200s -eli5: Attackers often use random, long domain names for their attack infrastructure. - This search looks at all the queries observed over the search time frame, and identifies - any domains being resolved with names that are greater that 2 times the standard - deviation. -entities: - - src how_to_implement: To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible there can be long domain names that are legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Exfiltration - - Commonly Used Port - mitre_technique_id: - - T1071 - nist: - - PR.PT - - DE.AE - - DE.CM -modification_date: '2020-01-22' -name: DNS Query Length With High Standard Deviation -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +type: ESCU references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution + by DNS.query DNS.record_type | `drop_dm_object_name("DNS")` | eval query_length + = len(query) | table query query_length record_type count | eventstats stdev(query_length) + AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) + | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` ' +known_false_positives: It's possible there can be long domain names that are legitimate. +tags: + analytics_story: + - Hidden Cobra Malware + - Suspicious DNS Traffic + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + cis20: + - CIS 8 + - CIS 12 + nist: + - PR.PT + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml b/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml new file mode 100644 index 0000000000..67fba352f1 --- /dev/null +++ b/detections/dns_query_requests_resolved_by_unauthorized_dns_servers.yml @@ -0,0 +1,39 @@ +name: DNS Query Requests Resolved by Unauthorized DNS Servers +id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 +version: 2 +date: '2020-01-17' +description: This search will detect DNS requests resolved by unauthorized DNS servers. + Legitimate DNS servers should be identified in the Enterprise Security Assets and + Identity Framework. +how_to_implement: To successfully implement this search you will need to ensure that + DNS data is populating the Network_Resolution data model. It also requires that + your DNS servers are identified correctly in the Assets and Identity table of Enterprise + Security. +type: ESCU +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution + where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src + DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` ' +known_false_positives: Legitimate DNS activity can be detected in this search. Investigate, + verify and update the list of authorized DNS servers as appropriate. +tags: + analytics_story: + - DNS Hijacking + - Command and Control + - Suspicious DNS Traffic + - Host Redirection + kill_chain_phases: + - Command and Control + cis20: + - CIS 1 + - CIS 3 + - CIS 8 + - CIS 12 + nist: + - ID.AM + - PR.DS + - PR.IP + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/dns_record_changed.yml b/detections/dns_record_changed.yml index fb54624042..c438de4e70 100644 --- a/detections/dns_record_changed.yml +++ b/detections/dns_record_changed.yml @@ -1,59 +1,10 @@ -asset_type: Endpoint -baselines: - - id: c096f721-8842-42ce-bfc7-74bd8c72b7c3 - name: Discover DNS records - type: splunk -confidence: medium -creation_date: '2019-02-14' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: DNS record changed +id: 44d3a43e-dcd5-49f7-8356-5209bb369065 +version: 2 +date: '2020-01-16' description: The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. -detect: - splunk: - correlation_rule: - lookups: - - discovered_dns_records - macros: - - dns_record_changed_filter - notable: - nes_fields: src - rule_description: The table represents a list of DNS records and their responses - for corporate domains that have recently changed - rule_title: DNS record changed - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| inputlookup discovered_dns_records.csv | rename answer as discovered_answer - | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as - type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution - where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query - | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] - | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer - | makemv current_answer | mvexpand current_answer | makemv discovered_answer - | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`' - suppress: - suppress_fields: src - suppress_period: 28800s -eli5: Using a lookup `discover_dns_records` generated by support search "Discover - DNS records" we check previous network traffic and make sure the responses have - not changed. -entities: - - src - - dest how_to_implement: "To successfully implement this search you will need to ensure that\ \ DNS data is populating the `Network_Resolution` data model. It also requires that\ \ the `discover_dns_record` lookup table be populated by the included support search\ @@ -67,47 +18,34 @@ how_to_implement: "To successfully implement this search you will need to ensure \ when configuring this detection search, and set the corresponding Playbook to\ \ active. \\\n(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\\\ \n" -id: 44d3a43e-dcd5-49f7-8356-5209bb369065 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: c096f721-8842-42ce-bfc7-74bd9a72c712 - name: DNS Hijack Enrichment - type: phantom +type: ESCU +author: Jose Hernandez, Splunk +search: '| inputlookup discovered_dns_records.csv | rename answer as discovered_answer + | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) + as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution + where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query + | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] + | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer + | makemv current_answer | mvexpand current_answer | makemv discovered_answer | + eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`' known_false_positives: Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 1 - - CIS 3 - - CIS 8 - - CIS 12 +tags: + analytics_story: + - DNS Hijacking kill_chain_phases: - - Command and Control - mitre_attack: - - Exfiltration - - Command and Control - - Defense Evasion + - Command and Control + cis20: + - CIS 1 + - CIS 3 + - CIS 8 + - CIS 12 nist: - - ID.AM - - PR.DS - - PR.IP - - DE.AE - - DE.CM -modification_date: '2020-01-16' -name: DNS record changed -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' + - ID.AM + - PR.DS + - PR.IP + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/dns_tunnelling.yml b/detections/dns_tunnelling.yml deleted file mode 100644 index a492f3faba..0000000000 --- a/detections/dns_tunnelling.yml +++ /dev/null @@ -1,128 +0,0 @@ -asset_type: Endpoint -confidence: low -creation_date: '2017-07-19' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search is used to detect DNS tunneling, by calculating the sum of - the length of DNS queries and DNS answers. The search also filters out potential - false positives by filtering out queries made to internal systems and the queries - originating from internal DNS, Web, and Email servers. Endpoints using DNS as a - method of transmission for data exfiltration, command and control, or evasion of - security controls can often be detected by noting an unusually large volume of DNS - traffic. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src - rule_description: Potential DNS tunnel detected from $src$ which may be exfiltrating - large data - rule_title: DNS tunnel detected on $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where - nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) - NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR - "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) - by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message - | eval length=len(message) | stats sum(length) as length by src | append [ - tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where - nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) - NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR - "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) - by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message - | eval message=if(message=="unknown","", message) | eval length=len(message) - | stats sum(length) as length by src ] | stats sum(length) as length by src - | where length > 10000' - suppress: - suppress_fields: src, query - suppress_period: 43200s -eli5: The search will calculate the distinct count and sum of the length of DNS queries - made and DNS answers received by a particular host to alert the analyst if the combined - length is greater than 10000, which is not typical behavior. -entities: - - src -how_to_implement: To successfully implement this search, we must ensure that DNS data - is being ingested and mapped to the appropriate fields in the Network_Resolution - data model. Fields like src_category are automatically provided by the Assets and - Identity Framework shipped with Splunk Enterprise Security. You will need to ensure - you are using the Assets and Identity Framework and populating the src_category - field. You will also need to enable the `cim_corporate_web_domain_search()` macro - which will essentially filter out the DNS queries made to the corporate web domains - to reduce alert fatigue. -id: 104658f4-afdc-499f-9719-17a43f9826f4 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible that normal DNS traffic will exhibit this behavior. - If an alert is generated, please investigate and validate as appropriate. The threshold - can also be modified to better suit your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Command and Control - - Exfiltration - - Commonly Used Port - nist: - - PR.PT - - PR.DS -modification_date: '2017-09-18' -name: Detection of DNS Tunnels -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/dragonfly_schtasks.yml b/detections/dragonfly_schtasks.yml deleted file mode 100644 index fdff4b89a9..0000000000 --- a/detections/dragonfly_schtasks.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-03-19' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for flags passed to schtasks.exe on the command-line - that indicate a task name associated with the Dragonfly threat actor was created - or deleted. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: This search looks for flags passed to schtasks.exe on the - command line that indicate that a task--whose name is associated with the - Dragonfly threat actor--has been created or deleted - rule_title: Scheduled task used by Dragonfly threat actor detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - macros: - - dragonfly_schtasks_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe by - Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - search (process=*delete* OR process=*create*) process=*reset* | `dragonfly_schtasks_filter`' - suppress: - suppress_fields: dest, process_name, process - suppress_period: 28800s -eli5: The search looks for execution of schtasks.exe with parameters that indicate - that a specific task "reset," whose name is associated with the Dragonfly threat - actor--has been created or deleted. Schtasks.exe is a native Windows program that - is used to schedule tasks on local or remote systems. Attackers often leverage this - capability to schedule the execution of commands or establish persistence. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: d5af132c-7c17-439c-9d31-13d55340f36c -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: No known false positives -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Scheduled Task - nist: - - PR.IP -modification_date: '2020-03-16' -name: Scheduled Task Name Used by Dragonfly Threat Actors -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/dump_lsass_via_comsvcs_dll.yml b/detections/dump_lsass_via_comsvcs_dll.yml index 77a2020584..4a1800d7b3 100644 --- a/detections/dump_lsass_via_comsvcs_dll.yml +++ b/detections/dump_lsass_via_comsvcs_dll.yml @@ -1,91 +1,35 @@ -asset_type: Endpoint -confidence: high -creation_date: '2020-02-21' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon +name: Dump LSASS via comsvcs DLL +id: 8943b567-f14d-4ee8-a0bb-2121d4ce3184 +version: 1 +date: '2020-02-21' description: Detect the usage of comsvcs.dll for dumping the lsass process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, process_name, process - rule_description: An attempt to dump credentials of lsass by $user$ was detected. - rule_title: Dump LSASS via comsvcs DLL - risk: - risk_object: user - risk_object_type: - - user - risk_score: 80 - macros: - - dump_lsass_via_comsvcs_dll_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* - Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `dump_lsass_via_comsvcs_dll_filter`' - suppress: - suppress_fields: process_name, dest - suppress_period: 86400s -eli5: LSASS is the Local Security Authority Subsystem Service, which is responsible for storing the user credentials. - There are multiple ways to attack LSASS. This search detects the usage of comsvcs.dll for dumping the LSASS process. -entities: - - dest how_to_implement: You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: 8943b567-f14d-4ee8-a0bb-2121d4ce3184 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk -known_false_positives: None identified. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_technique_id: - - T1003 - mitre_attack: - - Credential Access - - Credential Dumping - nist: - - DE.CM -modification_date: '2020-02-21' -name: Dump LSASS via comsvcs DLL -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss +type: ESCU references: - - https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ - - https://twitter.com/SBousseaden/status/1167417096374050817 -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +- https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/ +- https://twitter.com/SBousseaden/status/1167417096374050817 +author: Patrick Bareiss, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe + Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name + Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter`' +known_false_positives: None identified. +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 16 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/dyn_dns_web_traffic.yml b/detections/dyn_dns_web_traffic.yml deleted file mode 100644 index dd6a95015f..0000000000 --- a/detections/dyn_dns_web_traffic.yml +++ /dev/null @@ -1,125 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-09-06' -data_metadata: - data_models: - - Web - data_source: - - Web Traffic - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: This search looks for web connections to dynamic DNS providers. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, url, dest - rule_description: The host $src$ has been detected making a web request to - $url$, which is a listed as a dynamic DNS provider. - rule_title: Dynamic DNS web traffic detected on $src$. - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Web.url) - as url min(_time) as firstTime from datamodel=Web where Web.status=200 by - Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `dynamic_dns_web_traffic`' - suppress: - suppress_fields: src, url , dest - suppress_period: 86400s -eli5: This search looks for hosts in your environment that may be communicating with - a dynamic DNS provider. It checks each URL an endpoint is connecting to against - a list of dynamic DNS providers. It returns the source and destination IP address - of the web request, the URL requested, and the first time the event occurred. -entities: - - src -how_to_implement: 'This search requires you to be ingesting web-traffic logs. You - can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis - tool, such as Bro or Splunk Stream. The web data model must contain the URL being - requested, the IP address of the host initiating the request, and the destination - IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, - which contains a non-exhaustive list of dynamic DNS providers. Consider periodically - updating this local lookup file with new domains.\ - - This search produces fields (`isDynDNS`) that are not yet supported by ES Incident - Review and therefore cannot be viewed when a notable event is raised. These fields - contribute additional context to the notable. To see the additional metadata, add - the following fields, if not already present, to Incident Review - Event Attributes - (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. - **Label:** IsDynamicDNS, **Field:** isDynDNS\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: 134da869-e264-4a8f-8d7e-fcd01c18f301 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: 2f5b960b-71df-49c0-affc-74992ce60e45 - name: Investigate Web Activity From src_ip - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is possible that list of dynamic DNS providers is outdated - and/or that the URL being requested is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Command and Control - - Web Service - - Exfiltration Over Command and Control Channel - - Defense Evasion - nist: - - PR.IP - - DE.DP -modification_date: '2018-09-06' -name: Detect web traffic to dynamic domain providers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/ec2_excessive_runinstances_mltk.yml b/detections/ec2_excessive_runinstances_mltk.yml deleted file mode 100644 index 7ca29a42c5..0000000000 --- a/detections/ec2_excessive_runinstances_mltk.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fa5634df-fb05-4b4b-aba0-6115138bb1ba - name: Baseline of Excessive AWS Instances Launched by User - MLTK - type: splunk -confidence: medium -creation_date: '2019-11-14' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user successfully launches - an abnormally high number of instances. -detect: - splunk: - correlation_rule: - macros: - - ec2_excessive_runinstances_mltk_input_filter - notable: - nes_fields: src_user - rule_description: An abnormally high number of instances were launched by - a user within in a 10-minute window - rule_title: High Number of instances launched by $src_user$ - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: '*/10 * * * *' - earliest_time: -70m@m - latest_time: -10m@m - search: >- - sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` - | bucket span=10m _time - | stats count as instances_launched by _time src_user - | apply ec2_excessive_runinstances_v1 - | rename "IsOutlier(instances_launched)" as isOutlier - | where isOutlier=1 - suppress: - suppress_fields: src_user - suppress_period: 3600s -eli5: In this search, we query CloudTrail logs to look for events where an instance - is successfully launched by a particular user. Since we want to detect a high number - of instances launched within a short period, we create event buckets for 10-minute - windows. We then compare the total number of instances launched by a particular - user against the saved baseline data in the model ec2_excessive_runinstances_v1. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. The threshold value should be tuned to your environment. -id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: Many service accounts configured within an AWS infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify if this search alerted on a - human user. -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - DE.DP - - DE.AE -modification_date: '2019-11-14' -name: Abnormally High AWS Instances Launched by User - MLTK -original_authors: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/ec2_excessive_terminateinstances_mltk.yml b/detections/ec2_excessive_terminateinstances_mltk.yml deleted file mode 100644 index d174f005eb..0000000000 --- a/detections/ec2_excessive_terminateinstances_mltk.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: b28ed6de-e4ba-40f7-ae0a-93a088c774ab - name: Baseline of Excessive AWS Instances Terminated by User - MLTK - type: splunk -confidence: medium -creation_date: '2019-11-14' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user successfully terminates - an abnormally high number of instances. -detect: - splunk: - correlation_rule: - macros: - - ec2_excessive_terminateinstances_mltk_input_filter - notable: - nes_fields: src_user - rule_description: An abnormally high number of instances were terminated by - a user within in a 10-minute window - rule_title: High Number of instances terminated by $src_user$ - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: '*/10 * * * *' - earliest_time: -70m@m - latest_time: -10m@m - search: >- - sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` - | bucket span=10m _time - | stats count as instances_terminated by _time src_user - | apply ec2_excessive_terminateinstances_v1 - | rename "IsOutlier(instances_terminated)" as isOutlier - | where isOutlier=1 - suppress: - suppress_fields: src_user - suppress_period: 3600s -eli5: In this search, we query CloudTrail logs to look for events where an instance - is successfully terminated by a particular user. Since we want to detect a high number - of instances terminated within a short period, we create event buckets for 10-minute - windows. We then compare the total number of instances terminated by a particular - user against the saved baseline data in the model ec2_excessive_terminateinstances_v1. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. The threshold value should be tuned to your environment. -id: 1c02b86a-cd85-473e-a50b-014a9ac8fe3e -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: Many service accounts configured within an AWS infrastructure - are known to exhibit this behavior. Please adjust the threshold values and filter - out service accounts from the output. Always verify if this search alerted on a - human user. -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - DE.DP - - DE.AE -modification_date: '2019-11-14' -name: Abnormally High AWS Instances Terminated by User - MLTK -original_authors: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/ec2_instance_created_by_previously_unseen_user.yml b/detections/ec2_instance_created_by_previously_unseen_user.yml deleted file mode 100644 index 6d60fcf56a..0000000000 --- a/detections/ec2_instance_created_by_previously_unseen_user.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: 6c767ac0-0906-4355-9a83-927f5ee7bdad - name: Previously Seen EC2 Launches By User - type: splunk -confidence: medium -creation_date: '2018-03-15' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for EC2 instances being created by users who have not - created them before. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest - rule_description: The EC2 instance $dest$ was created by $user$. This user - has never created an EC2 instance before. - rule_title: EC2 Instance Created By Previously Unseen User $user$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail - eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime - latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as - arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats - min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup - previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), - "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType - as instanceType, responseElements.instancesSet.items{}.instanceId as dest, - userIdentity.arn as user | table _time, user, dest, instanceType - suppress: - suppress_fields: user, dest - suppress_period: 14400s -eli5: The subsearch returns the ARNs of all successful EC2 instance launches within - the last hour and then appends the historical data in the lookup file to those results. It - then recalculates the `firstTime` and `lastTime` field for each ARN and returns - only those ARNs that have first been seen in the past hour. This is combined with - the main search to return the time, user, and instance id of those systems. -entities: - - dest -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. This search works best when you run the "Previously Seen EC2 Launches By - User" support search once to create a history of previously seen ARNs. -id: 22773e84-bac0-4595-b086-20d3f735b4f1 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: It's possible that a user will start to create EC2 instances - when they haven't before for any number of reasons. Verify with the user that is - launching instances that this is the intended behavior. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-03-12' -name: EC2 Instance Started With Previously Unseen User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/ec2_instance_modified_by_previously_unseen_user.yml b/detections/ec2_instance_modified_by_previously_unseen_user.yml deleted file mode 100644 index 7703d45119..0000000000 --- a/detections/ec2_instance_modified_by_previously_unseen_user.yml +++ /dev/null @@ -1,93 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: 4d69091b-d975-4267-85df-888bd41034eb - name: Previously Seen EC2 Modifications By User - type: splunk -confidence: medium -creation_date: '2018-04-09' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for EC2 instances being modified by users who have - not previously modified them. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest - rule_description: The EC2 instance $dest$ was modified by $user$. This user - has never modified an EC2 instance before. - rule_title: EC2 Instance Modified By Previously Unseen User $user$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `ec2_modification_api_calls` [search sourcetype=aws:cloudtrail - `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime - latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as - arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats - min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup - previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), - "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest - responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn - | table _time, user, dest - suppress: - suppress_fields: user, dest - suppress_period: 14400s -eli5: The subsearch returns the ARNs of all successful EC2 instance modifications - within the last hour and then appends the historical data in the lookup file to - those results. EC2 modification APIs are defined by the macro `ec2_modification_api_calls`. - The search then recalculates the `firstTime` and `lastTime` field for each ARN and - returns only those ARNs that have first been seen in the past hour. This is combined - with the main search to return the time, user, and instance ID of those systems. -entities: - - dest -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. This search works best when you run the "Previously Seen EC2 Launches By - User" support search once to create a history of previously seen ARNs. To add or - remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -id: 56f91724-cf3f-4666-84e1-e3712fb41e76 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 - name: Get EC2 Instance Details by instanceId - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk -known_false_positives: It's possible that a new user will start to modify EC2 instances - when they haven't before for any number of reasons. Verify with the user that is - modifying instances that this is the intended behavior. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 1 - nist: - - ID.AM -modification_date: '2018-04-09' -name: EC2 Instance Modified With Previously Unseen User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/ec2_instance_modified_with_previously_unseen_user.yml b/detections/ec2_instance_modified_with_previously_unseen_user.yml new file mode 100644 index 0000000000..88e2e4f956 --- /dev/null +++ b/detections/ec2_instance_modified_with_previously_unseen_user.yml @@ -0,0 +1,34 @@ +name: EC2 Instance Modified With Previously Unseen User +id: 56f91724-cf3f-4666-84e1-e3712fb41e76 +version: 2 +date: '2018-04-09' +description: This search looks for EC2 instances being modified by users who have + not previously modified them. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. This search works best when you run the "Previously Seen EC2 Launches By + User" support search once to create a history of previously seen ARNs. To add or + remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` + errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime + by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user + | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup + previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), + "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId + | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter`' +known_false_positives: It's possible that a new user will start to modify EC2 instances + when they haven't before for any number of reasons. Verify with the user that is + modifying instances that this is the intended behavior. +tags: + analytics_story: + - Unusual AWS EC2 Modifications + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/ec2_instance_started_in_previously_unseen_region.yml b/detections/ec2_instance_started_in_previously_unseen_region.yml new file mode 100644 index 0000000000..ab52ff8feb --- /dev/null +++ b/detections/ec2_instance_started_in_previously_unseen_region.yml @@ -0,0 +1,36 @@ +name: EC2 Instance Started In Previously Unseen Region +id: ada0f478-84a8-4641-a3f3-d82362d6fd75 +version: 1 +date: '2018-02-23' +description: This search looks for CloudTrail events where an instance is started + in a particular region in the last one hour and then compares it to a lookup file + of previously seen regions where an instance was started +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. Run the "Previously seen AWS Regions" support search only once to create + of baseline of previously seen regions. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest + latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv + | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup + previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), + "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` + | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New + Region" | `ec2_instance_started_in_previously_unseen_region_filter`' +known_false_positives: It's possible that a user has unknowingly started an instance + in a new region. Please verify that this activity is legitimate. +tags: + analytics_story: + - AWS Cryptomining + - Suspicious AWS EC2 Activities + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 12 + nist: + - DE.DP + - DE.AE + security_domain: network + asset_type: AWS Instance diff --git a/detections/ec2_instance_started_with_previously_unseen_ami.yml b/detections/ec2_instance_started_with_previously_unseen_ami.yml index 3677473773..6362d82869 100644 --- a/detections/ec2_instance_started_with_previously_unseen_ami.yml +++ b/detections/ec2_instance_started_with_previously_unseen_ami.yml @@ -1,88 +1,36 @@ -asset_type: AWS Instance -baselines: - - id: bb1bd99d-1e93-45f1-9571-cfed42d372b9 - name: Previously Seen EC2 AMIs - type: splunk -confidence: medium -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: EC2 Instance Started With Previously Unseen AMI +id: 347ec301-601b-48b9-81aa-9ddf9c829dd3 +version: 1 +date: '2018-03-12' description: This search looks for EC2 instances being created with previously unseen AMIs. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The EC2 instance $dest$ was created with previously unused - AMI $amiID$ - rule_title: EC2 Instance Type $dest$ Created With New AMI - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail - eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime - latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId - | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup - append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) - as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime - >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` - | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId - | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType - as instanceType, responseElements.instancesSet.items{}.instanceId as dest, - userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as - amiID | table firstTime, lastTime, arn, amiID, dest, instanceType - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: The subsearch returns the AMI image ID of all successful EC2 instance launches - within the last hour and then appends the historical data from the lookup file to - those results. It then recalculates the earliest and latest seen time field for - each AMI image ID and returns only those AMI image IDs that have first been seen - in the past hour. This is combined with the main search to return the time, user, - and instance id of those systems. -entities: - - dest how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. -id: 347ec301-601b-48b9-81aa-9ddf9c829dd3 -investigations: - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances + errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime + by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId + as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) + as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv + | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId + | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType + as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn + as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, + lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter`' known_false_positives: After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: +tags: + analytics_story: + - AWS Cryptomining cis20: - - CIS 1 + - CIS 1 nist: - - ID.AM -modification_date: '2018-03-12' -name: EC2 Instance Started With Previously Unseen AMI -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/ec2_instance_started_with_previously_unseen_instance_type.yml b/detections/ec2_instance_started_with_previously_unseen_instance_type.yml index d4a2913c5f..4e7d2a6278 100644 --- a/detections/ec2_instance_started_with_previously_unseen_instance_type.yml +++ b/detections/ec2_instance_started_with_previously_unseen_instance_type.yml @@ -1,93 +1,36 @@ -asset_type: AWS Instance -baselines: - - id: b8f029f2-65a6-4d76-be98-dad1c9d59c45 - name: Previously Seen EC2 Instance Types - type: splunk -confidence: medium -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS +name: EC2 Instance Started With Previously Unseen Instance Type +id: 65541c80-03c7-4e05-83c8-1dcd57a2e1ad +version: 2 +date: '2020-02-07' description: This search looks for EC2 instances being created with previously unseen instance types. -detect: - splunk: - correlation_rule: - notable: - nes_fields: instanceType - rule_description: The EC2 instance type $instanceType$ was used for the first - time to create $dest$. - rule_title: New EC2 Instance Type $instanceType$ detected - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=RunInstances - [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success - | fillnull value="m1.small" requestParameters.instanceType - | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType - | rename requestParameters.instanceType as instanceType - | inputlookup append=t previously_seen_ec2_instance_types.csv - | stats min(earliest) as earliest max(latest) as latest by instanceType - | outputlookup previously_seen_ec2_instance_types.csv - | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) - | `security_content_ctime(earliest)` - | `security_content_ctime(latest)` - | where newType=1 - | rename instanceType as requestParameters.instanceType - | table requestParameters.instanceType] - | spath output=user userIdentity.arn - | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId - as dest | table _time, user, dest, instanceType - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: The subsearch returns the instance types of all successful EC2 instance launches - within the last hour and then appends the historical data in the lookup file to - those results. It then recalculates the earliest seen time field for each instance - type and returns only those instance types that has first been seen in the past - hour. This is combined with the main search to return the time, user, and instance - id of those systems. -entities: - - dest how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. -id: 65541c80-03c7-4e05-83c8-1dcd57a2e1ad -investigations: - - id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 - name: Get EC2 Launch Details - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances + errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats + earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType + | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv + | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup + previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), + "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` + | where newType=1 | rename instanceType as requestParameters.instanceType | table + requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType + as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table + _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter`' known_false_positives: It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: +tags: + analytics_story: + - AWS Cryptomining cis20: - - CIS 1 + - CIS 1 nist: - - ID.AM -modification_date: '2020-02-07' -name: EC2 Instance Started With Previously Unseen Instance Type -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/ec2_instance_started_with_previously_unseen_user.yml b/detections/ec2_instance_started_with_previously_unseen_user.yml new file mode 100644 index 0000000000..2eb9eec2d6 --- /dev/null +++ b/detections/ec2_instance_started_with_previously_unseen_user.yml @@ -0,0 +1,35 @@ +name: EC2 Instance Started With Previously Unseen User +id: 22773e84-bac0-4595-b086-20d3f735b4f1 +version: 1 +date: '2018-03-12' +description: This search looks for EC2 instances being created by users who have not + created them before. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. This search works best when you run the "Previously Seen EC2 Launches By + User" support search once to create a history of previously seen ARNs. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances + errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime + by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv + | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup + previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), + "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType + as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn + as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter`' +known_false_positives: It's possible that a user will start to create EC2 instances + when they haven't before for any number of reasons. Verify with the user that is + launching instances that this is the intended behavior. +tags: + analytics_story: + - AWS Cryptomining + - Suspicious AWS EC2 Activities + cis20: + - CIS 1 + nist: + - ID.AM + security_domain: endpoint + asset_type: AWS Instance diff --git a/detections/email_attachments_with_lots_of_spaces.yml b/detections/email_attachments_with_lots_of_spaces.yml new file mode 100644 index 0000000000..cf550ea395 --- /dev/null +++ b/detections/email_attachments_with_lots_of_spaces.yml @@ -0,0 +1,41 @@ +name: Email Attachments With Lots Of Spaces +id: 56e877a6-1455-4479-ada6-0550dc1e22f8 +version: 2 +date: '2017-09-19' +description: Attackers often use spaces as a means to obfuscate an attachment's file + extension. This search looks for messages with email attachments that have many + spaces within the file names. +how_to_implement: "You need to ingest data from emails. Specifically, the sender's\ + \ address and the file names of any attachments must be mapped to the Email data\ + \ model. The threshold ratio is set to 10%, but this value can be configured to\ + \ suit each environment. \\\n **Splunk Phantom Playbook Integration**\\\nIf Splunk\ + \ Phantom is also configured in your environment, a playbook called \"Suspicious\ + \ Email Attachment Investigate and Delete\" can be configured to run when any results\ + \ are found by this detection search. To use this integration, install the Phantom\ + \ App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname\ + \ to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring\ + \ this detection search. The notable event will be sent to Phantom and the playbook\ + \ will gather further information about the file attachment and its network behaviors.\ + \ If Phantom finds malicious behavior and an analyst approves of the results, the\ + \ email will be deleted from the user's inbox." +type: ESCU +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(All_Email.recipient) + as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email + where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` + | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio + >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter`' +known_false_positives: None at this time +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Emails + kill_chain_phases: + - Delivery + cis20: + - CIS 7 + nist: + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/email_attachments_with_spaces.yml b/detections/email_attachments_with_spaces.yml deleted file mode 100644 index 59f0724a18..0000000000 --- a/detections/email_attachments_with_spaces.yml +++ /dev/null @@ -1,90 +0,0 @@ -asset_type: Endpoint -channel: ESCU -confidence: high -creation_date: '2017-04-21' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: Attackers often use spaces as a means to obfuscate an attachment's file - extension. This search looks for messages with email attachments that have many - spaces within the file names. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_user, file_name - rule_description: 'The sender $src_user$ has sent an email with a suspicious - amount of spaces in the file name: $file_name$' - rule_title: Suspicious Email Attachment from $src_user$ - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address - min(_time) as firstTime max(_time) as lastTime from datamodel=Email where - All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` - | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search - space_ratio >= 0.1 | rex field=recipient_address "(?.*)@"' - suppress: - suppress_fields: src_user - suppress_period: 86400s -eli5: This search looks at any emails with file attachment names that contain many - spaces, relative to the length of the file name. Specifically, it checks to see - whether spaces make up more than 10% of the number of characters in the file name. - This percentage can be tuned for each environment. The search will output the message - ID of the email, the count, the sender and recipient addresses, the first and last - time this event was seen, and the space ratio of the file attachment name. -entities: - - src - - message_id -how_to_implement: "You need to ingest data from emails. Specifically, the sender's\ - \ address and the file names of any attachments must be mapped to the Email data\ - \ model. The threshold ratio is set to 10%, but this value can be configured to\ - \ suit each environment. \\\n **Splunk Phantom Playbook Integration**\\\nIf Splunk\ - \ Phantom is also configured in your environment, a playbook called \"Suspicious\ - \ Email Attachment Investigate and Delete\" can be configured to run when any results\ - \ are found by this detection search. To use this integration, install the Phantom\ - \ App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname\ - \ to the \"Phantom Instance\" field in the Adaptive Response Actions when configuring\ - \ this detection search. The notable event will be sent to Phantom and the playbook\ - \ will gather further information about the file attachment and its network behaviors.\ - \ If Phantom finds malicious behavior and an analyst approves of the results, the\ - \ email will be deleted from the user's inbox." -id: 56e877a6-1455-4479-ada6-0550dc1e22f8 -investigations: - - id: 3096f721-8842-42ce-2fc7-742d8372b712 - name: Suspicious Email Attachment Investigate and Delete - type: phantom -known_false_positives: None at this time -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Delivery - mitre_attack: [] - nist: - - PR.IP -modification_date: '2017-09-19' -name: Email Attachments With Lots Of Spaces -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/email_files_not_in_outlook_directory.yml b/detections/email_files_not_in_outlook_directory.yml deleted file mode 100644 index 53718f57ce..0000000000 --- a/detections/email_files_not_in_outlook_directory.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-12-13' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The search looks at the change-analysis data model and detects email - files created outside the normal Outlook directory. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_path, action, file_name - rule_description: 'The system $dest$ has email files outside of the normal - Outlook directory ' - rule_title: Email files created or modified on $dest$ that are not in the - normal Outlook directory - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - email_files_not_in_outlook_directory_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem - where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path - != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id - Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_not_in_outlook_directory_filter`' - suppress: - suppress_fields: dest, file_path - suppress_period: 86400s -eli5: In this search, we are looking for activities consistent with an adversary collecting - email data from local machines. The search will detect email files (files with .pst - or .ost extensions) created in directories other than the standard Outlook directory - (c:\users\username\My Documents\Outlook Files\. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records the file-system activity from your hosts to populate the Endpoint.Filesystem - data model node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report file-system - reads and writes. -id: ee18ed37-0802-4268-9435-b3b91aaa18xx -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Administrators and users sometimes prefer backing up their - email data by moving the email files into a different folder. These attempts will - be detected by the search. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Collection - - Email Collection -modification_date: '2020-03-23' -name: Email files written outside of the Outlook directory -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/email_files_written_outside_of_the_outlook_directory.yml b/detections/email_files_written_outside_of_the_outlook_directory.yml new file mode 100644 index 0000000000..3bda188598 --- /dev/null +++ b/detections/email_files_written_outside_of_the_outlook_directory.yml @@ -0,0 +1,36 @@ +name: Email files written outside of the Outlook directory +id: ee18ed37-0802-4268-9435-b3b91aaa18xx +version: 2 +date: '2020-03-23' +description: The search looks at the change-analysis data model and detects email + files created outside the normal Outlook directory. +how_to_implement: To successfully implement this search, you must be ingesting data + that records the file-system activity from your hosts to populate the Endpoint.Filesystem + data model node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. + The data used for this search is typically generated via logs that report file-system + reads and writes. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) + as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem + where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path + != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" + by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest + | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| + `email_files_written_outside_of_the_outlook_directory_filter` ' +known_false_positives: Administrators and users sometimes prefer backing up their + email data by moving the email files into a different folder. These attempts will + be detected by the search. +tags: + analytics_story: + - Collection and Staging + mitre_attack_id: + - T1114 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/email_servers_sending_high_volume_traffic_to_hosts.yml b/detections/email_servers_sending_high_volume_traffic_to_hosts.yml new file mode 100644 index 0000000000..ef37865c3b --- /dev/null +++ b/detections/email_servers_sending_high_volume_traffic_to_hosts.yml @@ -0,0 +1,50 @@ +name: Email servers sending high volume traffic to hosts +id: 7f5fb3e1-4209-4914-90db-0ec21b556378 +version: 1 +date: '2017-12-20' +description: This search looks for an increase of data transfers from your email server + to your clients. This could be indicative of a malicious actor collecting data using + your email server. +how_to_implement: This search requires you to be ingesting your network traffic and + populating the Network_Traffic data model. Your email servers must be categorized + as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold + and minimum_data_samples values based on the network traffic in your environment. + The "deviation_threshold" field is a multiplying factor to control how much variation + you're willing to tolerate. The "minimum_data_samples" field is the minimum number + of connections of data samples required for the statistic to be valid. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out + from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip + _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) + as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples + avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out + stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out + by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples + >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * + stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold + * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average + = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average + = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, + 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, + num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`' +known_false_positives: The false-positive rate will vary based on how you set the + deviation_threshold and data_samples values. Our recommendation is to adjust these + values based on your network traffic to and from your email servers. +tags: + analytics_story: + - Collection and Staging + mitre_attack_id: + - T1114 + - T1043 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 7 + nist: + - PR.PT + - DE.CM + - DE.AE + security_domain: network + asset_type: Endpoint diff --git a/detections/emailserver_high_volume_outbound_traffic_to_hosts.yml b/detections/emailserver_high_volume_outbound_traffic_to_hosts.yml deleted file mode 100644 index e662b8902a..0000000000 --- a/detections/emailserver_high_volume_outbound_traffic_to_hosts.yml +++ /dev/null @@ -1,120 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-12-20' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream -description: This search looks for an increase of data transfers from your email server - to your clients. This could be indicative of a malicious actor collecting data using - your email server. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest_ip - rule_description: High volume of traffic that originated from an email server - is being sent to $dest_ip$ - rule_title: High volume of traffic from an email server sent to $dest_ip$ - risk: - risk_object: dest_ip - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 0 * * * - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from - datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip - _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) - as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as - num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, - null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), - "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval - minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples - >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold - * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold - * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval - num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) - / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = - round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, - 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, - num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average' - suppress: - suppress_fields: dest_ip - suppress_period: 86400s -eli5: This search may look complex, but it's a neat representation of how statistics - can help you understand your dataset to bubble up events that are not normal compared - to its behavior. The search consists of three parts. The first part of the SPL fetches - the data you want to work on. In this search, we calculate the sum of bytes sent - and bytes_out from systems categorized as email_server to each host. We then calculate - the average and standard deviation for the bytes sent to all the hosts combined - and on a per-host basis. Then we set threshold values to deviation_threshold and - minimum_data_samples using eval statements. The "deviation_threshold" field is a - multiplying factor to control how much variation you're willing to tolerate. The - "minimum_data_samples" field is the minimum number of connections of data samples - required for the statistic to be valid. We then check for byte transfers that are - statistically significantly higher than normal. The search then gives IP address - of the host, the time of the increased byte transfer, how much data was transferred, - and the average amount of data transfer the email server normally sends to all hosts - and to this specific host. Finally, it includes the number of standard deviations - away the byte count was from these averages. -entities: - - dest_ip -how_to_implement: This search requires you to be ingesting your network traffic and - populating the Network_Traffic data model. Your email servers must be categorized - as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold - and minimum_data_samples values based on the network traffic in your environment. - The "deviation_threshold" field is a multiplying factor to control how much variation - you're willing to tolerate. The "minimum_data_samples" field is the minimum number - of connections of data samples required for the statistic to be valid. -id: 7f5fb3e1-4209-4914-90db-0ec21b556378 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: The false-positive rate will vary based on how you set the - deviation_threshold and data_samples values. Our recommendation is to adjust these - values based on your network traffic to and from your email servers. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Collection - - Email Collection - - Commonly Used Port - nist: - - PR.PT - - DE.CM - - DE.AE -modification_date: '2017-12-20' -name: Email servers sending high volume traffic to hosts -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/excessive_dns_failures.yml b/detections/excessive_dns_failures.yml index b5c64c15c2..d2e6d34058 100644 --- a/detections/excessive_dns_failures.yml +++ b/detections/excessive_dns_failures.yml @@ -1,107 +1,40 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro +name: Excessive DNS Failures +id: 104658f4-afdc-499e-9719-17243f9826f1 +version: 1 +date: '2017-09-18' description: This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, query - rule_description: This search identifies DNS query failures by counting the - number of DNS responses that do not indicate success and triggers on more - than 50 occurrences. - rule_title: Excessive DNS Failures - risk: - risk_object: src - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values("DNS.query") as queries from - datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" - "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" - "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| - lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where - isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT - rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries - by src| `get_asset(src)`| where count>50' - suppress: - suppress_fields: src,query - suppress_period: 43200s -eli5: This search looks at DNS traffic with a reply code that is NOT indicative of - a successful response. Numerous unsuccessful replies may be indicative of DNS protocol - tampering or other malicious activity. If more than 50 of these unsuccessful responses - are observed over the time frame of the search, a notable event will be generated. -entities: - - src how_to_implement: To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -id: 104658f4-afdc-499e-9719-17243f9826f1 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values("DNS.query") as queries + from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" + "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" + by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup + domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str + domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) + as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter`' known_false_positives: It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - - CIS 9 - - CIS 12 +tags: + analytics_story: + - Suspicious DNS Traffic + - Command and Control + mitre_attack_id: + - T1048 + - T1043 kill_chain_phases: - - Command and Control - mitre_attack: - - Exfiltration - - Exfiltration Over Alternative Protocol - - Command and Control - - Commonly Used Port + - Command and Control + cis20: + - CIS 8 + - CIS 9 + - CIS 12 nist: - - PR.PT - - DE.AE - - DE.CM -modification_date: '2017-09-18' -name: Excessive DNS Failures -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' + - PR.PT + - DE.AE + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/excessive_lockouts_from_endpoint.yml b/detections/excessive_lockouts_from_endpoint.yml deleted file mode 100644 index 3535c89c72..0000000000 --- a/detections/excessive_lockouts_from_endpoint.yml +++ /dev/null @@ -1,91 +0,0 @@ -asset_type: Windows -confidence: low -creation_date: '2017-08-17' -data_metadata: - data_models: - - Change - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search identifies endpoints that have caused a relatively high number - of account lockouts in a short period. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The system $dest$ has generated a high number of account - lockouts. - rule_title: $dest$ has generated a high number of account lockouts - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -4h@h - latest_time: -5m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management - All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` - |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search count > 5' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search queries the `Change.All_Changes` datamodel under the nodename is - `Account_Management` , where the result is "lockout", which indicates that an account - has been locked out. It then counts the number of times an endpoint has caused an - account lockout within a four hour window and displays those hosts with a count - greater than or equal to five. -entities: - - dest -how_to_implement: "You must ingest your Windows security event logs in the `Change`\ - \ datamodel under the nodename is `Account_Management`, for this search to execute\ - \ successfully. Please consider updating the cron schedule and the count of lockouts\ - \ you want to monitor, according to your environment. \\\n **Splunk>Phantom Playbook\ - \ Integration**\\\nIf Splunk>Phantom is also configured in your environment, a Playbook\ - \ called \"Excessive Account Lockouts Enrichment and Response\" can be configured\ - \ to run when any results are found by this detection search. The Playbook executes\ - \ the Contextual and Investigative searches in this Story, conducts additional information\ - \ gathering on Windows endpoints, and takes a response action to shut down the affected\ - \ endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,\ - \ add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response\ - \ Actions when configuring this detection search, and set the corresponding Playbook\ - \ to active. \\\n(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\\\ - \n" -id: c026e3dd-7e18-4abb-8f41-929e836efe74 -investigations: - - id: ab62b5c1-95d4-4e71-8fd7-53a55db33da4 - name: Excessive Account Lockouts Enrichment And Response - type: phantom -known_false_positives: It's possible that a widely used system, such as a kiosk, could - cause a large number of account lockouts. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: proyer@splunk.com - name: Philip Royer -mappings: - cis20: - - CIS 16 - mitre_attack: - - Initial Access - - Valid Accounts - nist: - - PR.IP -modification_date: '2019-04-18' -name: Detect Excessive Account Lockouts From Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -responses: [] -security_domain: access -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/excessive_user_account_lockouts.yml b/detections/excessive_user_account_lockouts.yml deleted file mode 100644 index 649484dc33..0000000000 --- a/detections/excessive_user_account_lockouts.yml +++ /dev/null @@ -1,99 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2017-08-17' -data_metadata: - data_models: - - Change - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search detects user accounts that have been locked out a relatively - high number of times in a short period. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: The account $user$ has been locked out an excessive number - of times - rule_title: $user$ locked account an excessive number of times - risk: - risk_object: user - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -4h@h - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management - All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` - |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search count > 5' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search queries the `Change.All_Changes` datamodel under the nodename is - `Account_Management` , where the result is "lockout", which indicates that an account - has been locked out. It then counts the number of times a user has caused an account - lockout within a four hour window and displays those users with a count greater - than or equal to five. -entities: - - user -how_to_implement: ou must ingest your Windows security event logs in the `Change` - datamodel under the nodename is `Account_Management`, for this search to execute - successfully. Please consider updating the cron schedule and the count of lockouts - you want to monitor, according to your environment. -id: 95a7f9a5-6096-437e-a19e-86f42ac609bd -investigations: - - id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb - name: Get Logon Rights Modifications For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505 - name: Get Logon Rights Modifications For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is possible that a legitimate user is experiencing an issue - causing multiple account login failures leading to lockouts. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 16 - mitre_attack: - - Initial Access - - Valid Accounts - nist: - - PR.IP -modification_date: '2019-03-01' -name: Detect Excessive User Account Lockouts -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/execution_of_file_with_multiple_extensions.yml b/detections/execution_of_file_with_multiple_extensions.yml new file mode 100644 index 0000000000..cf8be907df --- /dev/null +++ b/detections/execution_of_file_with_multiple_extensions.yml @@ -0,0 +1,37 @@ +name: Execution of File with Multiple Extensions +id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7 +version: 1 +date: '2018-11-02' +description: This search looks for processes launched from files that have double + extensions in the file name. This is typically done to obscure the "real" file extension + and make it appear as though the file being accessed is a data file, as opposed + to executable content. +how_to_implement: To successfully implement this search, you must be ingesting data + that records process activity from your hosts to populate the endpoint data model + in the processes node. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe + OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process + = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by + Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter`' +known_false_positives: None identified. +tags: + analytics_story: + - Windows File Extension and Association Abuse + mitre_attack_id: + - T1042 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - DE.CM + - PR.PT + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/execution_of_file_with_spaces_before_extension.yml b/detections/execution_of_file_with_spaces_before_extension.yml new file mode 100644 index 0000000000..c3f1e0f0af --- /dev/null +++ b/detections/execution_of_file_with_spaces_before_extension.yml @@ -0,0 +1,36 @@ +name: Execution of File With Spaces Before Extension +id: ab0353e6-a956-420b-b724-a8b4846d5d5a +version: 1 +date: '2018-01-26' +description: This search looks for processes launched from files with at least five + spaces in the name before the extension. This is typically done to obfuscate the + file extension by pushing it outside of the default view. +how_to_implement: To successfully implement this search, you must be ingesting data + that records process activity from your hosts to populate the endpoint data model + in the processes node. If you are using Sysmon, you must have at least version 6.0.4 + of the Sysmon TA. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process_path) + as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process = "* .*" by Processes.dest Processes.user Processes.process + Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter`' +known_false_positives: None identified. +tags: + analytics_story: + - Windows File Extension and Association Abuse + mitre_attack_id: + - T1042 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - DE.CM + - PR.PT + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/execution_of_nirsoft_tools.yml b/detections/execution_of_nirsoft_tools.yml deleted file mode 100644 index 3e1daf05cc..0000000000 --- a/detections/execution_of_nirsoft_tools.yml +++ /dev/null @@ -1,116 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-09-11' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for specific command-line arguments that may indicate - the execution of tools made by Nirsoft, which are legitimate, but may be abused - by attackers. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for specific arguments passed via the - command line and detects execution of tools built by NirSoft, which are - often abused by attackers. - rule_title: Potential abuse of NirSoft tools on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where - (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process - Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | - `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: The search looks for process-creation events accompanied by specific command-line - arguments ("scomma" and "stext"). These parameters may be leveraged by a set of - free, legitimate tools built by NirSoft. Attackers have been seen abusing the tools' - capabilities to steal passwords, set up key loggers, recover account information - from mail clients, and conduct other nefarious activities. The search will identify - the count, the first and last times a process is executed, the command-line arguments, - and the parent process. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: 1297fb80-f42a-4q4a-9c8b-78c061417cf6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You - should verfiy that the tool was used for a legitimate purpose. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Discovery - - Execution - - Lateral Movement - - Third-party Software - - Account Discovery - nist: - - PR.IP -modification_date: '2018-12-03' -name: Detection of tools built by NirSoft -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/extended_period_without_successful_netbackup_backups.yml b/detections/extended_period_without_successful_netbackup_backups.yml new file mode 100644 index 0000000000..f7cad2f1dd --- /dev/null +++ b/detections/extended_period_without_successful_netbackup_backups.yml @@ -0,0 +1,29 @@ +name: Extended Period Without Successful Netbackup Backups +id: a34aae96-ccf8-4aef-952c-3ea214444440 +version: 1 +date: '2017-09-12' +description: This search returns a list of hosts that have not successfully completed + a backup in over a week. +how_to_implement: To successfully implement this search you need to first obtain data + from your backup solution, either from the backup logs on your hosts, or from a + central server responsible for performing the backups. If you do not use Netbackup, + you can modify this search for your backup solution. Depending on how often you + backup your systems, you may want to modify how far in the past to look for a successful + backup, other than the default of seven days. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats + latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` + | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), + "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Monitor Backup Solution + cis20: + - CIS 10 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/file_with_samsam_extension.yml b/detections/file_with_samsam_extension.yml new file mode 100644 index 0000000000..50a2f06b78 --- /dev/null +++ b/detections/file_with_samsam_extension.yml @@ -0,0 +1,34 @@ +name: File with Samsam Extension +id: 02c6cfc2-ae66-4735-bfc7-6291da834cbf +version: 1 +date: '2018-12-14' +description: The search looks for file writes with extensions consistent with a SamSam + ransomware attack. +how_to_implement: You must be ingesting data that records file-system activity from + your hosts to populate the Endpoint file-system data-model node. If you are using + Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you + want to collect data. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) + as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex + field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin + OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos + OR file_extension=.keyxml | `file_with_samsam_extension_filter`' +known_false_positives: Because these extensions are not typically used in normal operations, + you should investigate all results. +tags: + analytics_story: + - SamSam Ransomware + kill_chain_phases: + - Installation + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/file_write_spikes.yml b/detections/file_write_spikes.yml deleted file mode 100644 index cdb8d28712..0000000000 --- a/detections/file_write_spikes.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: low -creation_date: '2017-08-20' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The search looks for a sharp increase in the number of files written - to a particular host -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: A sharp increase in file writes was detected on $dest - rule_title: Spike in file writes on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - macros: - - file_write_spikes_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -7d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where - Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` - | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time - >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search - isOutlier=1 | `file_write_spikes_filter`' - suppress: - suppress_fields: dest - suppress_period: 7200s -eli5: This search calculates counts the number of file modification events per hour - per host in your environment. It then takes the average and standard deviations - of those numbers and displays any hosts with more than 20 events that have over - four times the standard deviation more than the average number of file modifications. -entities: - - dest -how_to_implement: In order to implement this search, you must populate the Endpoint - file-system data model node. This is typically populated via endpoint detection - and response products, such as Carbon Black or endpoint data sources such as Sysmon. - The data used for this search is typically generated via logs that report reads - and writes to the file system. -id: fdb0f805-74e4-4539-8c00-618927333aae -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: It is important to understand that if you happen to install - any new applications on your hosts or are copying a large number of files, you can - expect to see a large increase of file modifications. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - DE.CM -modification_date: '2020-03-16' -name: Spike in File Writes -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/first_time_seen_cmd_line.yml b/detections/first_time_seen_cmd_line.yml deleted file mode 100644 index 058b722144..0000000000 --- a/detections/first_time_seen_cmd_line.yml +++ /dev/null @@ -1,141 +0,0 @@ -asset_type: Endpoint -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23 - name: Previously seen command line arguments - type: splunk -confidence: medium -creation_date: '2018-04-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for command-line arguments that use a `/c` parameter - to execute a command that has not previously been seen. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: The system $dest$ executed a command-line argument, $process$, - that has not previously been seen. - rule_title: First-time seen command-line argument was detected on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - first_time_seen_cmd_line_filter - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process - = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name - Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) - as lastTime from datamodel=Endpoint.Processes where Processes.process_name - = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` - | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) - as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments - | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), - 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table process] | `first_time_seen_cmd_line_filter` ' - suppress: - suppress_fields: dest, process - suppress_period: 86400s -eli5: The subsearch returns all events where `cmd.exe` was used with a `/c` parameter - in the command-line arguments to execute other commands/programs. It appends the - historical data to those results in the lookup file. Next, it recalculates the `firstTime` - and `lastTime` field for command-line execution and outputs this data to the lookup - file to update the local cache. It returns only those events that have first been - seen in the past one hour. This is combined with the main search to return the time, - user, destination, process, parent process, and value of the command-line argument. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must be ingesting - logs with both the process name and command line from your endpoints. The complete - process name with command-line arguments are mapped to the "process" field in the - Endpoint data model. Please make sure you run the support search "Previously seen - command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a - historical baseline of all command-line arguments. You must also validate this list. - For the search to do accurate calculation, ensure the search scheduling is the same - value as the `relative_time` evaluation function. -id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Legitimate programs can also use command-line arguments to - execute. Please verify the command-line arguments to check what command/program - is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - Scripting - - Persistence - - Command-Line Interface - mitre_technique_id: - - T1059 - - T1117 - - T1202 - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2020-03-02' -name: First time seen command line argument -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.1' diff --git a/detections/first_time_seen_command_line_argument.yml b/detections/first_time_seen_command_line_argument.yml new file mode 100644 index 0000000000..4e65aa9096 --- /dev/null +++ b/detections/first_time_seen_command_line_argument.yml @@ -0,0 +1,56 @@ +name: First time seen command line argument +id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 +version: 4 +date: '2020-03-02' +description: This search looks for command-line arguments that use a `/c` parameter + to execute a command that has not previously been seen. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must be ingesting + logs with both the process name and command line from your endpoints. The complete + process name with command-line arguments are mapped to the "process" field in the + Endpoint data model. Please make sure you run the support search "Previously seen + command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a + historical baseline of all command-line arguments. You must also validate this list. + For the search to do accurate calculation, ensure the search scheduling is the same + value as the `relative_time` evaluation function. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe + Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name + Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` + earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process + | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments + | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup + previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), + "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` ' +known_false_positives: Legitimate programs can also use command-line arguments to + execute. Please verify the command-line arguments to check what command/program + is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` + macro to exclude legitimate parent_process_name +tags: + analytics_story: + - DHS Report TA18-074A + - Suspicious Command-Line Executions + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Hidden Cobra Malware + mitre_attack_id: + - T1064 + - T1059 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/first_time_seen_running_windows_service.yml b/detections/first_time_seen_running_windows_service.yml index a6cb4849bb..7700039828 100644 --- a/detections/first_time_seen_running_windows_service.yml +++ b/detections/first_time_seen_running_windows_service.yml @@ -1,103 +1,46 @@ -asset_type: Endpoint -baselines: - - id: 64ce0ade-cb01-4678-bddd-d31c0b175394 - name: Previously Seen Running Windows Services - type: splunk -confidence: medium -creation_date: '2018-07-22' -data_metadata: - data_eventtypes: - - wineventlog_system - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows +name: First Time Seen Running Windows Service +id: 823136f2-d755-4b6d-ae04-372b486a5808 +version: 2 +date: '2020-01-13' description: This search looks for the first time a Windows service is seen running in your environment. -detect: - splunk: - correlation_rule: - notable: - nes_fields: service_name - rule_description: The service $service_name$ is running on $dest$. This is - the first time this service has been run on any system. - rule_title: First Time Seen Windows Service $service_name$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" [ search eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | inputlookup append=t previously_seen_running_windows_services | stats min(firstTime) as firstTime max(lastTime) as lastTime by service_name | outputlookup previously_seen_running_windows_services| eval serviceStatus=if(firstTime >= relative_time(now(),"-60m@m"), "First time seen Windows service","Previously seen Windows service") | where serviceStatus="First time seen Windows service"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table service_name]| table _time dest service_name - suppress: - suppress_fields: service_name, dest - suppress_period: 86400s -eli5: 'This search looks for a change in the status of a Windows service and extracts - the name of the service and the action taken by the service. Then the cache file - of previously seen Windows services is added to the search. At this point, the search - takes two different paths: the first updates the cache file with the latest information - and the second searches for services that have never before been seen. It returns - the time, the Windows host name, and the service name.' -entities: - - dest how_to_implement: While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, - should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -id: 823136f2-d755-4b6d-ae04-372b486a5808 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk + should be run before this search to create the baseline of known Windows services. + Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`wineventlog_system` signature_id=7036 | rename param1 as service_name | + rename param2 as action | search action="running" [ search `wineventlog_system` + signature_id=7036 | rename param1 as service_name | rename param2 as action | search + action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime + by service_name | inputlookup append=t previously_seen_running_windows_services + | stats min(firstTime) as firstTime max(lastTime) as lastTime by service_name | + outputlookup previously_seen_running_windows_services| eval serviceStatus=if(firstTime + >= relative_time(now(),"-60m@m"), "First time seen Windows service","Previously + seen Windows service") | where serviceStatus="First time seen Windows service"| + `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table + service_name]| table _time dest service_name | `first_time_seen_running_windows_service_filter`' known_false_positives: A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 2 - - CIS 9 +tags: + analytics_story: + - Windows Service Abuse + - Orangeworm Attack Group + mitre_attack_id: + - T1050 kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Execution - - New Service - mitre_technique_id: - - T1050 + - Installation + - Actions on Objectives + cis20: + - CIS 2 + - CIS 9 nist: - - ID.AM - - PR.DS - - PR.AC - - DE.AE -modification_date: '2020-01-13' -name: First Time Seen Running Windows Service -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - ID.AM + - PR.DS + - PR.AC + - DE.AE + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/fsutil_deleting_journals.yml b/detections/fsutil_deleting_journals.yml deleted file mode 100644 index fb022b8a5e..0000000000 --- a/detections/fsutil_deleting_journals.yml +++ /dev/null @@ -1,116 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-06-27' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The fsutil.exe application is a legitimate Windows utility used to perform - tasks related to the file allocation table (FAT) and NTFS file systems. The update - sequence number (USN) change journal provides a log of all changes made to the files - on the disk. This search looks for fsutil.exe deleting the USN journal. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: The system $dest$ deleted its NTFS journals. - rule_title: File System Journal Deleted on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - search process="*deletejournal*" AND process="*usn*"' - suppress: - suppress_fields: dest,user,process_name - suppress_period: 14400s -eli5: This search looks for the execution of fsutil.exe with command-line arguments - to delete the USN journal. The search returns the count of the number of times it's - seen this process execution with these arguments, the first and last time it's seen - this behavior, the hosts it was executed on, and the user context under which it - was executed. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: b6e0ff70-b122-4227-9368-4cf322ab43c3 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 6 - - CIS 8 - - CIS 10 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Indicator Removal on Host - nist: - - DE.CM - - PR.PT - - DE.AE - - DE.DP - - PR.IP -modification_date: '2018-12-03' -name: USN Journal Deletion -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/gcp_gcr_container_uploaded.yml b/detections/gcp_gcr_container_uploaded.yml new file mode 100644 index 0000000000..40c531d147 --- /dev/null +++ b/detections/gcp_gcr_container_uploaded.yml @@ -0,0 +1,27 @@ +name: GCP GCR container uploaded +id: 4f00ca88-e766-4605-ac65-ae51c9fd185b +version: 1 +date: '2020-02-20' +description: This search show information on uploaded containers including source + user, account, action, bucket name event name, http user agent, message and destination + path. +how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), + then configure stackdriver and set a subpub subscription to be imported to Splunk. + You must also install Cloud Infrastructure data model. Please also customize the + `container_implant_gcp_detection_filter` macro to filter out the false positives. +type: ESCU +references: [] +author: Rod Soto, Rico Valdez, Splunk +search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage + where Storage.event_name=storage.objects.create by Storage.src_user Storage.account + Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg + Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` ' +known_false_positives: Uploading container is a normal behavior from developers or + users with access to container registry. GCP GCR registers container upload as a + Storage event, this search must be considered under the context of CONTAINER upload + creation which automatically generates a bucket entry for destination path. +tags: + analytics_story: + - Container Implantation Monitoring and Investigation + security_domain: threat + asset_type: GCP GCR Container diff --git a/detections/gcp_kubernetes_cluster_scan_detection.yml b/detections/gcp_kubernetes_cluster_scan_detection.yml new file mode 100644 index 0000000000..3a666e58db --- /dev/null +++ b/detections/gcp_kubernetes_cluster_scan_detection.yml @@ -0,0 +1,31 @@ +name: GCP Kubernetes cluster scan detection +id: db5957ec-0144-4c56-b512-9dccbe7a2d26 +version: 1 +date: '2020-04-15' +description: This search provides information of unauthenticated requests via user + agent, and authentication data against Kubernetes cluster +how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), + then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection + to filter out FPs. +type: ESCU +references: [] +author: Rod Soto, Splunk +search: '`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 + data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid + "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" + | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) + as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name + values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) + as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name + as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | + `gcp_kubernetes_cluster_scan_detection_filter` ' +known_false_positives: Not all unauthenticated requests are malicious, but frequency, + User Agent and source IPs will provide context. +tags: + analytics_story: + - Kubernetes Scanning Activity + kill_chain_phases: + - Reconnaissance + security_domain: threat + asset_type: GCP Kubernetes cluster diff --git a/detections/hiding_files_and_directories_with_attrib_exe.yml b/detections/hiding_files_and_directories_with_attrib_exe.yml new file mode 100644 index 0000000000..84db36db2f --- /dev/null +++ b/detections/hiding_files_and_directories_with_attrib_exe.yml @@ -0,0 +1,35 @@ +name: Hiding Files And Directories With Attrib exe +id: c77162d3-f93c-45cc-80c8-22f6b5264g9f +version: 3 +date: '2020-03-16' +description: Attackers leverage an existing Windows binary, attrib.exe, to mark specific + as hidden by using specific flags so that the victim does not see the file. The + search looks for specific command-line arguments to detect the use of attrib.exe + to hide files. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) + as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe + (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user + Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| + `hiding_files_and_directories_with_attrib_exe_filter` ' +known_false_positives: 'Some applications and users may legitimately use attrib.exe + to interact with the files. ' +tags: + analytics_story: + - Windows Defense Evasion Tactics + - Windows Persistence Techniques + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/hosts_file_modification.yml b/detections/hosts_file_modification.yml deleted file mode 100644 index 81a4e357eb..0000000000 --- a/detections/hosts_file_modification.yml +++ /dev/null @@ -1,106 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-06-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: The search looks for modifications to the hosts file on all Windows endpoints - across your environment. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file modification was noted for the hosts file on $dest$. - rule_title: Modification of hosts file detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path - Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts - AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)`' - suppress: - suppress_fields: dest,user - suppress_period: 86400s -eli5: The hosts file is present on both Windows and Linux endpoints. The purpose of - the hosts file is to provide a mapping between hostnames and IP addresses, the same - way DNS is used to provide such a mapping. However, the information in the hosts - file takes precedence over information received via DNS and a DNS query will not - be issued if the hostname of interest is found in the hosts file. As such, attackers - have been observed adding entries to the host file to override any DNS resolution. - For this reason, it is useful to monitor for changes to this file, which typically - do not occur very often in legitimate cases. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records the file-system activity from your hosts to populate the Endpoint.Filesystem - data model node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report file-system - reads and writes. -id: 06a6fc63-a72d-41dc-8736-7e3dd9612116 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There may be legitimate reasons for system administrators to - add entries to this file. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 8 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Exfiltration - nist: - - PR.IP - - PR.PT - - PR.AC - - DE.AE - - DE.CM -modification_date: '2018-11-02' -name: Windows hosts file modification -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/hosts_receiving_high_volume_network_traffic_from_email_servers.yml b/detections/hosts_receiving_high_volume_network_traffic_from_email_servers.yml deleted file mode 100644 index 45ee8c9f06..0000000000 --- a/detections/hosts_receiving_high_volume_network_traffic_from_email_servers.yml +++ /dev/null @@ -1,122 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-12-20' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream -description: This search looks for an increase of data transfers from your email server - to your clients. This could be indicative of a malicious actor collecting data using - your email server. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip - rule_description: $src_ip$ receiving high volume of traffic that originated - from an email server - rule_title: High volume traffic from email server received by $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 0 * * * - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from - datamodel=Network_Traffic where All_Traffic.dest_category=email_server by - All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | - eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in - | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), - "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < - relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in - by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where - num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold - * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold - * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval - num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) - / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, - _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, - num_standard_deviations_away_from_client_average' - suppress: - suppress_fields: src_ip - suppress_period: 86400s -eli5: This search may look complex, but it's a neat representation of how statistics - can help you understand your dataset to bubble up events that are not normal compared - to its behavior. The search consists of three parts. The first part of the SPL fetches - the data you want to work on. In this search, we calculate the sum of bytes sent - and bytes_out from systems categorized as email_server to each host. We then calculate - the average and standard deviation for the bytes sent to all the hosts combined - and on a per-host basis. Then we set threshold values to deviation_threshold and - minimum_data_samples using eval statements. The "deviation_threshold" field is a - multiplying factor to control how much variation you're willing to tolerate. The - "minimum_data_samples" field is the minimum number of connections of data samples - required for the statistic to be valid. We then check for byte transfers that are - statistically significantly higher than normal. The search then gives IP address - of the host, the time of the increased byte transfer, how much data was transferred, - and the average amount of data transfer the email server normally sends to all hosts - and to this specific host. Finally, it includes the number of standard deviations - away the byte count was from these averages. -entities: - - src_ip -how_to_implement: This search requires you to be ingesting your network traffic and - populating the Network_Traffic data model. Your email servers must be categorized - as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold - and minimum_data_samples values based on the network traffic in your environment. - The "deviation_threshold" field is a multiplying factor to control how much variation - you're willing to tolerate. The "minimum_data_samples" field is the minimum number - of connections of data samples required for the statistic to be valid. -id: 7f5fb3e1-4209-4914-90db-0ec21b556368 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: The false-positive rate will vary based on how you set the - deviation_threshold and data_samples values. Our recommendation is to adjust these - values based on your network traffic to and from your email servers. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Collection - - Commonly Used Port - nist: - - PR.PT - - DE.CM - - DE.AE -modification_date: '2017-12-20' -name: Hosts receiving high volume of network traffic from email server -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml b/detections/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml new file mode 100644 index 0000000000..faaf133bf6 --- /dev/null +++ b/detections/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml @@ -0,0 +1,49 @@ +name: Hosts receiving high volume of network traffic from email server +id: 7f5fb3e1-4209-4914-90db-0ec21b556368 +version: 1 +date: '2017-12-20' +description: This search looks for an increase of data transfers from your email server + to your clients. This could be indicative of a malicious actor collecting data using + your email server. +how_to_implement: This search requires you to be ingesting your network traffic and + populating the Network_Traffic data model. Your email servers must be categorized + as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold + and minimum_data_samples values based on the network traffic in your environment. + The "deviation_threshold" field is a multiplying factor to control how much variation + you're willing to tolerate. The "minimum_data_samples" field is the minimum number + of connections of data samples required for the statistic to be valid. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in + from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip + _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) + as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples + avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in + stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in + by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples + >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) + AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) + AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average + = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average + = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) + | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, + num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`' +known_false_positives: The false-positive rate will vary based on how you set the + deviation_threshold and data_samples values. Our recommendation is to adjust these + values based on your network traffic to and from your email servers. +tags: + analytics_story: + - Collection and Staging + mitre_attack_id: + - T1043 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 7 + nist: + - PR.PT + - DE.CM + - DE.AE + security_domain: network + asset_type: Endpoint diff --git a/detections/identify_new_user_accounts.yml b/detections/identify_new_user_accounts.yml new file mode 100644 index 0000000000..3e287d1e8c --- /dev/null +++ b/detections/identify_new_user_accounts.yml @@ -0,0 +1,31 @@ +name: Identify New User Accounts +id: 475b9e27-17e4-46e2-b7e2-648221be3b89 +version: 1 +date: '2017-09-12' +description: This detection search will help profile user accounts in your environment + by identifying newly created accounts that have been added to your network in the + past week. +how_to_implement: To successfully implement this search, you need to be populating + the Enterprise Security Identity_Management data model in the assets and identity + framework. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, + "Accounts created in last week") | search empStatus="Accounts created in last week"| + `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity + empStatus endDate startDate | `identify_new_user_accounts_filter`' +known_false_positives: If the Identity_Management data model is not updated regularly, + this search could give you false positive alerts. Please consider this and investigate + appropriately. +tags: + analytics_story: + - Account Monitoring and Controls + mitre_attack_id: + - T1136 + cis20: + - CIS 16 + nist: + - PR.IP + security_domain: access + asset_type: Domain Server diff --git a/detections/kubernetes_aws_scan_fingerprint.yml b/detections/kubernetes_aws_scan_fingerprint.yml deleted file mode 100644 index 801fddcc9e..0000000000 --- a/detections/kubernetes_aws_scan_fingerprint.yml +++ /dev/null @@ -1,74 +0,0 @@ -asset_type: Amazon EKS Kubernetes cluster -confidence: high -creation_date: '2020-03-24' -data_metadata: - data_source: - - AWS CloudWatch EKS Logs - data_sourcetypes: - - aws:cloudwatchlogs:eks - providing_technologies: - - AWS -description: This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, cluster_name, http_user_agent - rule_description: This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS - rule_title: Amazon EKS Kubernetes cluster scan detection - risk: - risk_object: cluster_name - risk_object_type: - - other - risk_score: 70 - macros: - - kubernetes_aws_scan_fingerprint_detection - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: 'sourcetype="aws:cloudwatchlogs:eks" "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`kubernetes_aws_scan_fingerprint_detection`' -eli5: In this search we can detect unauthenticated web requests against an EKS cluster, by looking at k8s authentication data, user agent and source IPs. -entities: - - cluster_name - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs - inputs. -id: 294c4686-63dd-4fe6-93a2-ca807626704a -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: a636cca4-7434-4a15-a278-c70734938e39 - name: Amazon EKS Kubernetes activity by src_ip - type: splunk -known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -mappings: - mitre_attack: - - Discovery - kill_chain_phases: - - Reconnaissance - mitre_technique_id: - - T1190 -modification_date: '2020-04-15' -name: Amazon EKS Kubernetes cluster scan detection -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/kubernetes_gcp_scan_fingerprint_attack.yml b/detections/kubernetes_gcp_scan_fingerprint_attack.yml deleted file mode 100644 index 06f58f180a..0000000000 --- a/detections/kubernetes_gcp_scan_fingerprint_attack.yml +++ /dev/null @@ -1,72 +0,0 @@ -asset_type: GCP Kubernetes cluster -confidence: high -creation_date: '2020-03-24' -data_metadata: - data_source: - - GCP Stackdriver logs - data_sourcetypes: - - google:gcp:pubsub:message - providing_technologies: - - GCP -description: This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, - rule_description: Detect scanning attempts against a GCP Kubernetes cluster - rule_title: GCP Kubernetes cluster scan detection - risk: - risk_object: cluster_name - risk_object_type: - - other - risk_score: 70 - macros: - - kubernetes_gcp_scan_fingerprint_detection - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kubernetes_gcp_scan_fingerprint_detection` -eli5: In this search we can detect unauthenticated web requests and possible attack against a GCP cluster, by looking at k8s authentication data, user agent, source IPs and destionation -entities: - - cluster_name - - src_ip -how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -id: db5957ec-0144-4c56-b512-9dccbe7a2d26 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: c00e7626-92cc-4e06-9a51-b6db0a50bd1f - name: GCP Kubernetes activity by src_ip - type: splunk -known_false_positives: Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -mappings: - mitre_attack: - - Discovery - kill_chain_phases: - - Reconnaissance - mitre_technique_id: - - T1190 -modification_date: '2020-04-15' -name: GCP Kubernetes cluster scan detection -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/kubernetes_pods_aws_scan_fingerprint.yml b/detections/kubernetes_pods_aws_scan_fingerprint.yml deleted file mode 100644 index b23731ebc6..0000000000 --- a/detections/kubernetes_pods_aws_scan_fingerprint.yml +++ /dev/null @@ -1,72 +0,0 @@ -asset_type: Amazon EKS Kubernetes cluster Pod -confidence: medium -creation_date: '2020-03-24' -data_metadata: - data_source: - - AWS CloudWatch EKS Logs - data_sourcetypes: - - aws:cloudwatchlogs:eks - providing_technologies: - - AWS -description: This search provides detection information on unauthenticated requests against Kubernetes' Pods API -detect: - splunk: - correlation_rule: - notable: - nes_fields: user.username, sourceIPs, verb, http_user_agent, group_name, requestURI - rule_description: This search provides detection information on unauthenticated requests against Kubernetes' Pods API - rule_title: Amazon EKS Kubernetes Pod scan detection - risk: - risk_object: cluster_name - risk_object_type: - - other - risk_score: 70 - macros: - - kubernetes_pods_aws_scan_fingerprint_detection - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="aws:cloudwatchlogs:eks" "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kubernetes_pods_aws_scan_fingerprint_detection` -eli5: In this search we can detect unauthenticated web requests against an EKS cluster Pod, by looking at k8s authentication data, user agent and source IPs and API direct request. -entities: - - cluster_name - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. -id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: a636cca4-7434-4a15-a278-c70734938e39 - name: Amazon EKS Kubernetes activity by src_ip - type: splunk -known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -mappings: - mitre_attack: - - Discovery - kill_chain_phases: - - Reconnaissance - mitre_technique_id: - - T1190 -modification_date: '2020-04-15' -name: Amazon EKS Kubernetes Pod scan detection -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/large_icmp_outbound.yml b/detections/large_icmp_outbound.yml deleted file mode 100644 index 4c17f11b73..0000000000 --- a/detections/large_icmp_outbound.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-06-01' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream - - Palo Alto Firewall -description: This search looks for outbound ICMP packets with a packet size larger - than 1,000 bytes. Various threat actors have been known to use ICMP as a command - and control channel for their attack infrastructure. Large ICMP packets from an - endpoint to a remote host may be indicative of this activity. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest_ip - rule_description: Large outbound ICMP packet detected. - rule_title: Large ICMP packet from $src_ip$ to $dest_ip$ detected - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) - as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic - where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp - OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip - All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 - AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`' - suppress: - suppress_fields: src_ip - suppress_period: 28800s -eli5: This search works by looking at fields in the Network_Traffic data model, which - is populated by various firewalls and passive networking monitoring technologies. - Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination - that is external to your organization. -entities: - - src_ip -how_to_implement: 'In order to run this search effectively, we highly recommend that - you leverage the Assets and Identity framework. It is important that you have a - good understanding of how your network segments are designed and that you are able - to distinguish internal from external address space. Add a category named `internal` - to the CIDRs that host the company''s assets in the `assets_by_cidr.csv` lookup - file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. - More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. - This search also requires you to be ingesting your network traffic and populating - the Network_Traffic data model' -id: e9c102de-4d43-42a7-b1c8-8062ea297419 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot - networking issues and ensure the proper flow of traffic. As such, it is possible - that a large ICMP packet could be perfectly legitimate. If large ICMP packets are - associated with command and control traffic, there will typically be a large number - of these packets observed over time. If the search is providing a large number of - false positives, you can modify the search to adjust the byte threshold or whitelist - specific IP addresses, as necessary. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 9 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Standard Non-Application Layer Protocol - nist: - - DE.AE -modification_date: '2018-06-01' -name: Detect Large Outbound ICMP Packets -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/large_volume_of_dns_any_queries.yml b/detections/large_volume_of_dns_any_queries.yml new file mode 100644 index 0000000000..d480bab82b --- /dev/null +++ b/detections/large_volume_of_dns_any_queries.yml @@ -0,0 +1,31 @@ +name: Large Volume of DNS ANY Queries +id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb +version: 1 +date: '2017-09-20' +description: The search is used to identify attempts to use your DNS Infrastructure + for DDoS purposes via a DNS amplification attack leveraging ANY queries. +how_to_implement: To successfully implement this search you must ensure that DNS data + is populating the Network_Resolution data model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution + where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" + | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`' +known_false_positives: Legitimate ANY requests may trigger this search, however it + is unusual to see a large volume of them under typical circumstances. You may modify + the threshold in the search to better suit your environment. +tags: + analytics_story: + - DNS Amplification Attacks + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 11 + - CIS 12 + nist: + - PR.PT + - DE.AE + - PR.IP + security_domain: network + asset_type: DNS Servers diff --git a/detections/lnk_executing_a_process.yml b/detections/lnk_executing_a_process.yml deleted file mode 100644 index 5288ef531c..0000000000 --- a/detections/lnk_executing_a_process.yml +++ /dev/null @@ -1,94 +0,0 @@ -asset_type: Endpoint -baselines: [] -confidence: high -creation_date: '2019-04-29' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` - executing a process. This is common behavior used by various spear phishing tools. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, file_name - rule_description: suspicious LNK file from $file_name$ is executing a process - $process_name$ on $dest$ - rule_title: LNK file $file_name$ is executing process $process_name$ on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - macros: - - lnk_executing_a_process_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" - AND (Filesystem.file_path="C:\\Users*" OR Filesystem.file_path="*Local\\Temp*") by - _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path - Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | - rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` - count FROM datamodel=Endpoint.Processes where Processes.process_name=* by - _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name - Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` - | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest - process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, - process_name, process, process_path, file_hash | `lnk_executing_a_process_filter`' - suppress: - suppress_fields: dest,file_name - suppress_period: 86400s -eli5: In this search, we are essentially trying to detect if a LNK file created under - the C:\User* or *\Local\Temp\* directory structures is launching a process with - in 1 hour of its creation. LNK files or also known as Windows shortcut files are - commonly associated with phishing and are a [preferred method used for exploitation](https://www.fireeye.com/blog/threat-research/2017/04/fin7-phishing-lnk.html). -entities: - - dest -how_to_implement: You must be ingesting data that records filesystem and process activity - from your hosts to populate the Endpoint data model. This is typically populated - via endpoint detection-and-response products, such as Carbon Black, or endpoint - data sources, such as Sysmon. -id: 5d814af1-1041-47b5-a9ac-d754e82e9a26 -investigations: [] -known_false_positives: This detection should yield little or no false positive results. - It is uncommon for LNK files to execute process from temporary or user directories. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 7 - - CIS 8 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Initial Access - - Spearphishing Attachment - nist: - - ID.AM - - PR.DS -modification_date: '2020-03-16' -name: Suspicious LNK file launching a process -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/local_admin_account_creation.yml b/detections/local_admin_account_creation.yml deleted file mode 100644 index 7823101d52..0000000000 --- a/detections/local_admin_account_creation.yml +++ /dev/null @@ -1,126 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-03-26' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search looks for newly created accounts that have been elevated - to local administrators. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user,src_user, dest - rule_description: The new user account $user$ was created on $dest$ by $src_user$. - rule_title: New local admin account $user$ created by $src_user$. - risk: - risk_object: user - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 9 * * * - earliest_time: -1440m@m - latest_time: -10m@m - search: eventtype = wineventlog_security signature_id=4720 OR (signature_id=4732 - Group_Name= Administrators) | transaction Security_ID connected=false maxspan=180m | search - signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID - Group_Name src_user Message - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search looks for Windows Event Code 4720 (account creation) and 4732 (account - added to a security-enabled local group), where the group name is "Administrators", - and determines whether they are generated for the same user's Security ID within - three hours of each other. It will return the user account that was added, the - Security ID, the group name to which the user was added, the account name of the - user who initiated the action, and the subsequent message returned. -entities: - - user -how_to_implement: 'You must be ingesting Windows Security logs. You must also enable - the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. - Additionally, this search requires you to enable your Group Management Audit Logs - in your Local Windows Security Policy and to be ingesting those logs. More information - on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. - Finally, please make sure that the local administrator group name is "Administrators" - to be able to look for the right group membership changes.\ - - This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not - yet supported by ES Incident Review and therefore cannot be viewed when a notable - event is raised. These fields contribute additional context to the notable. To see - the additional metadata, add the following fields, if not already present, to Incident - Review - Event Attributes (Configure > Incident Management > Incident Review Settings - > Add New Entry):\\n1. **Label:** Security ID, **Field:** Security_ID\ - - 1. \ - - 1. **Label:** Group Name, **Field:** Group_Name\ - - 1. \ - - 1. **Label:** Message, **Field:** Message\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: b25f6f62-0712-43c1-b203-083231ffd97d -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: The activity may be legitimate. For this reason, it's best - to verify the account with an administrator and ask whether there was a valid service - request for the account creation. If your local administrator group name is not - "Administrators", this search may generate an excessive number of false positives -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - - Command and Control - mitre_attack: - - Valid Accounts - - Defense Evasion - - Persistence - nist: - - PR.AC - - DE.CM -modification_date: '2019-02-28' -name: Detect New Local Admin account -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/long_dns_text_response.yml b/detections/long_dns_text_response.yml deleted file mode 100644 index 6e61e544f1..0000000000 --- a/detections/long_dns_text_response.yml +++ /dev/null @@ -1,120 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-06-18' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search is used to detect attempts to use DNS tunneling, by calculating - the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission - for data exfiltration, command and control, or evasion of security controls can - often be detected by noting unusually large volumes of DNS traffic. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, query - rule_description: A DNS TXT record response of over 100 characters was detected. - rule_title: Long DNS TXT Record Response - risk: - risk_object: src - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Resolution where DNS.message_type=response - AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` - | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" - anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First - Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination - IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last - Time"' - suppress: - suppress_fields: src - suppress_period: 86400s -eli5: This search uses the Network_Resolution data model and gathers all the answers - to DNS queries for TXT records. The query then looks at the answer section and calculates - the length of the answer. The search will then return information for those responses - that exceed 100 characters in length. -entities: - - src -how_to_implement: To successfully implement this search you need to ingest data from - your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, - this query requires that the DNS data model is populated with information regarding - the DNS record type that is being returned as well as the data in the answer section - of the protocol. -id: 05437c07-62f5-452e-afdc-04dd44815bb9 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible that legitimate TXT record responses can be long - enough to trigger this search. You can modify the packet threshold for this search - to help mitigate false positives. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - - CIS 12 - - CIS 13 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Exfiltration - - Commonly Used Port - nist: - - PR.DS - - PR.PT - - DE.AE - - DE.CM -modification_date: '2017-09-18' -name: Detect Long DNS TXT Record Response -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/macos___re_opened_applications.yml b/detections/macos___re_opened_applications.yml new file mode 100644 index 0000000000..8174060ca0 --- /dev/null +++ b/detections/macos___re_opened_applications.yml @@ -0,0 +1,37 @@ +name: MacOS - Re-opened Applications +id: 40bb64f9-f619-4e3d-8732-328d40377c4b +version: 1 +date: '2020-02-07' +description: This search looks for processes referencing the plist files that determine + which applications are re-opened when a user reboots their machine. +how_to_implement: In order to properly run this search, Splunk needs to ingest process + data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) + pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be + deployed across your indexers and universal forwarders in order to have the data + populate the Endpoint data model. +type: ESCU +references: [] +author: Jamie Windley, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `macos___re_opened_applications_filter`' +known_false_positives: At this stage, there are no known false positives. During testing, + no process events refering the com.apple.loginwindow.plist files were observed during + normal operation of re-opening applications on reboot. Therefore, it can be asumed + that any occurences of this in the process events would be worth investigating. + In the event that the legitimate modification by the system of these files is in + fact logged to the process log, then the process_name of that process can be whitelisted. +tags: + kill_chain_phases: + - Installation + - Command and Control + cis20: + - CIS 8 + nist: + - DE.DP + - DE.CM + security_domain: threat + asset_type: Endpoint diff --git a/detections/macos_re-opened_applications.yml b/detections/macos_re-opened_applications.yml deleted file mode 100644 index 55b5635484..0000000000 --- a/detections/macos_re-opened_applications.yml +++ /dev/null @@ -1,71 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2020-02-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint - providing_technologies: - - OSquery -description: This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. -detect: - splunk: - correlation_rule: - notable: - nes_fields: host - rule_description: Observed process referencing MacOS com.apple.loginwindow.plist files for Host $host$ - rule_title: Possible attempt to establish persistence on $host$ - risk: - risk_object: host - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: host - suppress_period: 3600s -eli5: The search looks at the Endpoint data model to identify any MacOS process events referencing a property list file which determines which applications are "re-opened" during startup. This could indicate a malicious attempt to establish persistence on the system. -entities: - - host -how_to_implement: In order to properly run this search, Splunk needs to ingest process data - from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) - pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must - be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. -id: 40bb64f9-f619-4e3d-8732-328d40377c4b -known_false_positives: At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. -maintainers: - - company: Splunk - email: jwindley@splunk.com - name: Jamie Windley -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Installation - - Command and Control - mitre_attack: - - Persistence - mitre_technique_id: - - T1164 - nist: - - DE.DP - - DE.CM -modification_date: '2020-02-07' -name: MacOS - Re-opened Applications -original_authors: - - company: Splunk - email: jwindley@splunk.com - name: Jamie Windley -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/malicious_application_shimming_via_registry.yml b/detections/malicious_application_shimming_via_registry.yml deleted file mode 100644 index 8ea5335ec2..0000000000 --- a/detections/malicious_application_shimming_via_registry.yml +++ /dev/null @@ -1,113 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-27' -data_metadata: - data_models: - - Change_Analysis - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: This search looks for registry activity associated with application compatibility - shims, which can be leveraged by attackers for various nefarious purposes. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user - rule_description: A registry key that is used for persistence on Windows was - modified on $dest$ by $user$ - rule_title: Registry Key Associated With SHIM databases on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry - AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR - All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") - by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, - All_Changes.object_path | `drop_dm_object_name("All_Changes")`' - suppress: - suppress_fields: dest,object_path - suppress_period: 86400s -eli5: In this search, we look for modifications to registry keys used for shim databases - on Microsoft platforms via the object_category and object_path field in the Change_Analysis - data model and give you the destination, command used to initiate the change, the - user who conducted this activity, the resource affected(object), and the whole path - of the object. An application compatibility shim is a small library that transparently - intercepts an API (via hooking), changes the parameters passed, handles the operation - itself, or redirects the operation elsewhere, such as additional code stored on - a system. This capability can be also leveraged by attackers to create and store - malicious files in a shim database as observed in CARBANAK backdoor. -entities: - - dest -how_to_implement: To successfully implement this search, you must populate the Change_Analysis - data model. This is typically populated via endpoint detection and response products, - such as Carbon Black or other endpoint data sources such as Sysmon. The data used - for this search is typically generated via logs that report reads and writes to - the registry. -id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There are many legitimate applications that leverage shim databases - for compatibility purposes for legacy applications -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Application Shimming - nist: - - PR.PT - - DE.CM -modification_date: '2017-09-15' -name: Registry Keys for Creating SHIM Databases -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/malicious_powershell_process___connect_to_internet_with_hidden_window.yml b/detections/malicious_powershell_process___connect_to_internet_with_hidden_window.yml new file mode 100644 index 0000000000..c1b4b686bc --- /dev/null +++ b/detections/malicious_powershell_process___connect_to_internet_with_hidden_window.yml @@ -0,0 +1,46 @@ +name: Malicious PowerShell Process - Connect To Internet With Hidden Window +id: ee18ed37-0802-4268-9435-b3b91aaa18db +version: 3 +date: '2018-12-03' +description: This search looks for PowerShell processes started with parameters to + modify the execution policy of the run, run in a hidden window, and connect to the + Internet. This combination of command-line options is suspicious because it's overriding + the default PowerShell execution policy, attempts to hide its activity from the + user, and connects to the Internet. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | search process="*-Exec*" process="*-WindowStyle*" process="*hidden*" process="*New-Object*" + process="*System.Net.WebClient*" | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter`' +known_false_positives: Legitimate process can have this combination of command-line + options, but it's not common. +tags: + analytics_story: + - Malicious PowerShell + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 7 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/malicious_powershell_process___encoded_command.yml b/detections/malicious_powershell_process___encoded_command.yml new file mode 100644 index 0000000000..f3351b5136 --- /dev/null +++ b/detections/malicious_powershell_process___encoded_command.yml @@ -0,0 +1,41 @@ +name: Malicious PowerShell Process - Encoded Command +id: c4db14d9-7909-48b4-a054-aa14d89dbb19 +version: 3 +date: '2018-12-03' +description: This search looks for PowerShell processes that have encoded the script + within the command-line. Malware has been seen using this parameter, as it obfuscates + the code and makes it relatively easy to pass a script on the command-line. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | search process=*-EncodedCommand* OR process=*-enc* | `malicious_powershell_process___encoded_command_filter`' +known_false_positives: System administrators may use this option, but it's not common. +tags: + analytics_story: + - Malicious PowerShell + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 7 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/malicious_powershell_process___execution_policy_bypass.yml b/detections/malicious_powershell_process___execution_policy_bypass.yml new file mode 100644 index 0000000000..11e12bdc54 --- /dev/null +++ b/detections/malicious_powershell_process___execution_policy_bypass.yml @@ -0,0 +1,44 @@ +name: Malicious PowerShell Process - Execution Policy Bypass +id: 9be56c82-b1cc-4318-87eb-d138afaaca39 +version: 3 +date: '2018-12-03' +description: This search looks for PowerShell processes started with parameters used + to bypass the local execution policy for scripts. These parameters are often observed + in attacks leveraging PowerShell scripts as they override the default PowerShell + execution policy. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process_id) as + process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name=powershell.exe AND (Processes.process="* -ex*" OR Processes.process="* + bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter`' +known_false_positives: There may be legitimate reasons to bypass the PowerShell execution + policy. The PowerShell script being run with this parameter should be validated + to ensure that it is legitimate. +tags: + analytics_story: + - DHS Report TA18-074A + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 7 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml b/detections/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml new file mode 100644 index 0000000000..0839c9c547 --- /dev/null +++ b/detections/malicious_powershell_process___multiple_suspicious_command_line_arguments.yml @@ -0,0 +1,46 @@ +name: Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +id: 2cdb91d2-542c-497f-b252-be495e71f38c +version: 3 +date: '2018-12-03' +description: This search looks for PowerShell processes started with a base64 encoded + command-line passed to it, with parameters to modify the execution policy for the + process, and those that prevent the display of an interactive prompt to the user. + This combination of command-line options is suspicious because it overrides the + default PowerShell execution policy, attempts to hide itself from the user, and + passes an encoded script to be run on the command-line. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| + search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI* + | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter`' +known_false_positives: Legitimate process can have this combination of command-line + options, but it's not common. +tags: + analytics_story: + - Malicious PowerShell + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 7 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/malicious_powershell_process_connect_to_internet_with_hidden_window.yml b/detections/malicious_powershell_process_connect_to_internet_with_hidden_window.yml deleted file mode 100644 index 438dac1cd0..0000000000 --- a/detections/malicious_powershell_process_connect_to_internet_with_hidden_window.yml +++ /dev/null @@ -1,125 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-18' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for PowerShell processes started with parameters to - modify the execution policy of the run, run in a hidden window, and connect to the - Internet. This combination of command-line options is suspicious because it's overriding - the default PowerShell execution policy, attempts to hide its activity from the - user, and connects to the Internet. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: The system $dest$ executed a PowerShell process that connects - to the Internet with a hidden window. - rule_title: Malicious PowerShell Process detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - search process="*-Exec*" process="*-WindowStyle*" process="*hidden*" process="*New-Object*" - process="*System.Net.WebClient*"' - suppress: - suppress_fields: process_name, dest - suppress_period: 86400s -eli5: This search looks for PowerShell processes running with specific command-line - arguments that indicate that the process will download a file from the Internet - without display anything to the user. The search for "*-Exec*" is to check and see - if the default execution policy for PowerShell is being overridden on the command-line. - The search for "*-WindowStyle*" and "*hidden*" are to see if the window that would - normally be displayed will be hidden from the user instead. Finally, the search - for "*New-Object*" and "*System.Net.WebClient*" are there to check to see if a PowerShell - object that can be used to download files will be created. This search will return - the host, the user the process ran under, the process and it's command-line arguments, - the number of times it's seen this process, and the first and last times it saw - this process. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: ee18ed37-0802-4268-9435-b3b91aaa18db -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Legitimate process can have this combination of command-line - options, but it's not common. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2018-12-03' -name: Malicious PowerShell Process - Connect To Internet With Hidden Window -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/malicious_powershell_process_encoded_command.yml b/detections/malicious_powershell_process_encoded_command.yml deleted file mode 100644 index 2723244b9e..0000000000 --- a/detections/malicious_powershell_process_encoded_command.yml +++ /dev/null @@ -1,113 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-18' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for PowerShell processes that have encoded the script - within the command-line. Malware has been seen using this parameter, as it obfuscates - the code and makes it relatively easy to pass a script on the command-line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: The system $dest$ executed a PowerShell process that has - an encoded command on the command-line - rule_title: PowerShell process with an encoded command detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | - search process=*-EncodedCommand* OR process=*-enc*' - suppress: - suppress_fields: dest, user, process_name - suppress_period: 14400s -eli5: This search looks for PowerShell processes that are passing encoded commands - on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible - flags that can be used to pass base64 encoded commands to PowerShell. This search - will return the host, the user the process ran under, the process and it's command-line - arguments, the number of times it's seen this process, and the first and last times - it saw this process. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: c4db14d9-7909-48b4-a054-aa14d89dbb19 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: System administrators may use this option, but it's not common. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2018-12-03' -name: Malicious PowerShell Process - Encoded Command -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/malicious_powershell_process_execpolicy.yml b/detections/malicious_powershell_process_execpolicy.yml deleted file mode 100644 index 8d5590b727..0000000000 --- a/detections/malicious_powershell_process_execpolicy.yml +++ /dev/null @@ -1,121 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-03-19' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for PowerShell processes started with parameters used - to bypass the local execution policy for scripts. These parameters are often observed - in attacks leveraging PowerShell scripts as they override the default PowerShell - execution policy. -detect: - splunk: - correlation_rule: - notable: - drilldown_name: View powershell process information on $dest$ - drilldown_search: '| from datamodel:Endpoint.Processes | search dest="$dest$" process_id=$process_id$' - nes_fields: dest, user, process_name - rule_description: The system $dest$ executed a PowerShell process with parameters - to bypass the local execution policy. - rule_title: PowerShell process with -executionpolicy bypass detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 50 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, - values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=powershell.exe AND (Processes.process="* -ex*" OR Processes.process="* bypass *") by - Processes.process_id, Processes.user, Processes.dest - | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process_name - suppress_period: 14400s -eli5: This search looks for PowerShell processes that were launched using a parameter - designed to bypass the local PowerShell execution policy. By default, the policy - is set to "Restricted," which disables the execution of PowerShell scripts. In environments - that make heavy use of PowerShell, the policy can be set to allow only scripts signed - by a trusted publisher. Malicious PowerShell use almost always includes the parameter - `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting - command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, - can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for - `* -ex*`, which should catch all variations of this parameter, followed by the keyword - `bypass`. This search will return the host, the user the process ran under, the - process and its command-line arguments, the number of times it has seen this process, - and the first and last times it saw this process. -entities: - - dest - - process_id - - process - - parent_process_id -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: 9be56c82-b1cc-4318-87eb-d138afaaca39 -investigations: - - id: d8362a34-b78a-4364-9733-59b505f5b8d5 - name: Get Process Registry Activity - type: splunk - - id: 6a9ad4d9-6ef2-4b85-953f-a37ab256acd5 - name: Get Process File Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk -known_false_positives: There may be legitimate reasons to bypass the PowerShell execution - policy. The PowerShell script being run with this parameter should be validated - to ensure that it is legitimate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2018-12-03' -name: Malicious PowerShell Process - Execution Policy Bypass -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/malicious_powershell_process_multiple_suspicious_arguments.yml b/detections/malicious_powershell_process_multiple_suspicious_arguments.yml deleted file mode 100644 index 8627097c10..0000000000 --- a/detections/malicious_powershell_process_multiple_suspicious_arguments.yml +++ /dev/null @@ -1,123 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-18' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for PowerShell processes started with a base64 encoded - command-line passed to it, with parameters to modify the execution policy for the - process, and those that prevent the display of an interactive prompt to the user. - This combination of command-line options is suspicious because it overrides the - default PowerShell execution policy, attempts to hide itself from the user, and - passes an encoded script to be run on the command-line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process, process_name - rule_description: The system $dest$ executed a PowerShell that had an encoded - command on the command-line, attempted to bypass local execution policy, - and prevented the display of an interactive prompt to the user. - rule_title: PowerShell process with multiple suspicious command-line arguments - detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 50 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI*' - suppress: - suppress_fields: dest, process_name - suppress_period: 14400s -eli5: This search looks for PowerShell processes that have a number of suspicious - flags on the command-line. It is looking for flags are passing encoded commands - on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible - flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` - flag looks to see it the default execution policy of PowerShell is being overridden, - while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive - process, so the user doesn't know about the process. This search will return the - host, the user the process ran under, the process and it's command-line arguments, - the number of times it's seen this process, and the first and last times it saw - this process. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: 2cdb91d2-542c-497f-b252-be495e71f38c -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Legitimate process can have this combination of command-line - options, but it's not common. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2018-12-03' -name: Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/malicious_powershell_process_obfuscation_techniques.yml b/detections/malicious_powershell_process_obfuscation_techniques.yml deleted file mode 100644 index 20ce13cf37..0000000000 --- a/detections/malicious_powershell_process_obfuscation_techniques.yml +++ /dev/null @@ -1,117 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-04-25' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for PowerShell processes launched with arguments that - have characters indicative of obfuscation on the command-line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name, process - rule_description: The system $dest$ executed a PowerShell process that has - evidence of obfuscation on the command-line - rule_title: PowerShell process with an obfuscation techniques detected on - $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - eval num_obfuscation = (mvcount(split(process, "`"))-1) + (mvcount(split(process, - "^"))-1) | `malicious_powershell_process_obfuscation_techniques_output_filter` | search num_obfuscation > 0' - suppress: - suppress_fields: dest,process_name,process - suppress_period: 14400s -eli5: This search looks for PowerShell processes that are passing command-line arguments - with unusual characters (backticks and carets) that are PowerShell specific escape - characters. Attackers use this obfuscation technique since it does not affect the - functionality of PowerShell and it will bypass standard security controls that look - for straight up malicious strings and commands. The search counts the occurrence - of these obfuscation characters and lists out destination IPs running these PowerShell - commands. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: cde75cf6-3c7a-4dd6-af01-27cdb4511fd4 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: These characters might be legitimately on the command-line, - but it is not common. -maintainers: - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 8 - kill_chain_phases: - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - - PowerShell - - Scripting - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2020-01-17' -name: Malicious PowerShell Process With Obfuscation Techniques -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/malicious_powershell_process_with_obfuscation_techniques.yml b/detections/malicious_powershell_process_with_obfuscation_techniques.yml new file mode 100644 index 0000000000..d92fe3f4f0 --- /dev/null +++ b/detections/malicious_powershell_process_with_obfuscation_techniques.yml @@ -0,0 +1,43 @@ +name: Malicious PowerShell Process With Obfuscation Techniques +id: cde75cf6-3c7a-4dd6-af01-27cdb4511fd4 +version: 2 +date: '2020-01-17' +description: This search looks for PowerShell processes launched with arguments that + have characters indicative of obfuscation on the command-line. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest + Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, + "`"))-1) + (mvcount(split(process, "^"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` + | search num_obfuscation > 0' +known_false_positives: These characters might be legitimately on the command-line, + but it is not common. +tags: + analytics_story: + - Malicious PowerShell + mitre_attack_id: + - T1086 + - T1064 + kill_chain_phases: + - Command and Control + - Actions on Objectives + cis20: + - CIS 3 + - CIS 7 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/malicious_requests_to_exploit_jboss_servers.yml b/detections/malicious_requests_to_exploit_jboss_servers.yml deleted file mode 100644 index 9a517bdc51..0000000000 --- a/detections/malicious_requests_to_exploit_jboss_servers.yml +++ /dev/null @@ -1,100 +0,0 @@ -asset_type: Web Server -confidence: high -creation_date: '2016-10-04' -data_metadata: - data_models: - - Web - data_source: - - Network Communications - - Web Server - providing_technologies: - - Splunk Stream - - Palo Alto Firewall - - Apache - - Bro -description: This search is used to detect malicious HTTP requests crafted to exploit - jmx-console in JBoss servers. The malicious requests have a long URL length, as - the payload is embedded in the URL. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, dest_ip - rule_description: A search for detecting malicious requests made to exploit - jmx-console in JBoss servers. The bad requests have a long url length since - it serves the payload via the url - rule_title: Detected malicious requests to exploit JBoss servers - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") - by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" - AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime' - suppress: - suppress_fields: dest,url,src - suppress_period: 14400s -eli5: This search looks for HTTP requests for a URL that has been used to exploit - JBoss servers. -entities: - - dest -how_to_implement: You must ingest data from the web server or capture network data - that contains web specific information with solutions such as Bro or Splunk Stream, - and populating the Web data model -id: c8bff7a4-11ea-4416-a27d-c5bca472913d -investigations: - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: No known false positives for this detection. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 12 - - CIS 4 - - CIS 18 - kill_chain_phases: - - Delivery - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - nist: - - ID.RA - - PR.PT - - PR.IP - - DE.AE - - PR.MA - - DE.CM -modification_date: '2017-09-23' -name: Detect malicious requests to exploit JBoss servers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/monitor_dns_for_brand_abuse.yml b/detections/monitor_dns_for_brand_abuse.yml new file mode 100644 index 0000000000..6176c2b120 --- /dev/null +++ b/detections/monitor_dns_for_brand_abuse.yml @@ -0,0 +1,28 @@ +name: Monitor DNS For Brand Abuse +id: 24dd17b1-e2fb-4c31-878c-d4f746595bfa +version: 1 +date: '2017-09-23' +description: This search looks for DNS requests for faux domains similar to the domains + that you want to have monitored for abuse. +how_to_implement: You need to ingest data from your DNS logs. Specifically you must + ingest the domain that is being queried and the IP of the host originating the request. + Ideally, you should also be ingesting the answer to the query and the query type. + This approach allows you to also create your own localized passive DNS capability + which can aid you in future investigations. You also need to have run the search + "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that + will be checked for. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) + as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` + | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter`' +known_false_positives: None at this time +tags: + analytics_story: + - Brand Monitoring + kill_chain_phases: + - Delivery + - Actions on Objectives + security_domain: network + asset_type: Endpoint diff --git a/detections/monitor_email_for_brand_abuse.yml b/detections/monitor_email_for_brand_abuse.yml new file mode 100644 index 0000000000..aa409d05fa --- /dev/null +++ b/detections/monitor_email_for_brand_abuse.yml @@ -0,0 +1,33 @@ +name: Monitor Email For Brand Abuse +id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 +version: 2 +date: '2018-01-05' +description: This search looks for emails claiming to be sent from a domain similar + to one that you want to have monitored for abuse. +how_to_implement: You need to ingest email header data. Specifically the sender's + address (src_user) must be populated. You also need to have run the search "ESCU + - DNSTwist Domain Names", which creates the permutations of the domain that will + be checked for. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as + recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email + by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval + temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true + brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true + | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`' +known_false_positives: None at this time +tags: + analytics_story: + - Brand Monitoring + - Suspicious Emails + kill_chain_phases: + - Delivery + cis20: + - CIS 7 + nist: + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/monitor_registry_keys_for_print_monitors.yml b/detections/monitor_registry_keys_for_print_monitors.yml new file mode 100644 index 0000000000..dad8911dab --- /dev/null +++ b/detections/monitor_registry_keys_for_print_monitors.yml @@ -0,0 +1,40 @@ +name: Monitor Registry Keys for Print Monitors +id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc +version: 1 +date: '2018-11-02' +description: This search looks for registry activity associated with modifications + to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this + scenario, an attacker can load an arbitrary .dll into the print-monitor registry + by giving the full path name to the after.dll. The system will execute the .dll + with elevated (SYSTEM) permissions and will persist after reboot. +how_to_implement: To successfully implement this search, you must be ingesting data + that records registry activity from your hosts to populate the endpoint data model + in the registry node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. + The data used for this search is typically generated via logs that report registry + modifications. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND + Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, + Registry.registry_key_name Registry.status Registry.user Registry.registry_path + Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter`' +known_false_positives: You will encounter noise from legitimate print-monitor registry + entries. +tags: + analytics_story: + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + - CIS 5 + nist: + - PR.PT + - DE.CM + - PR.AC + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/monitor_web_traffic_for_brand_abuse.yml b/detections/monitor_web_traffic_for_brand_abuse.yml new file mode 100644 index 0000000000..0386c2f7fb --- /dev/null +++ b/detections/monitor_web_traffic_for_brand_abuse.yml @@ -0,0 +1,28 @@ +name: Monitor Web Traffic For Brand Abuse +id: 134da869-e264-4a8f-8d7e-fcd0ec88f301 +version: 1 +date: '2017-09-23' +description: This search looks for Web requests to faux domains similar to the one + that you want to have monitored for abuse. +how_to_implement: You need to ingest data from your web traffic. This can be accomplished + by indexing data from a web proxy, or using a network traffic analysis tool, such + as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain + Names", which creates the permutations of the domain that will be checked for. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) + as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` + | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`' +known_false_positives: None at this time +tags: + analytics_story: + - Brand Monitoring + kill_chain_phases: + - Delivery + cis20: + - CIS 7 + nist: + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/mshta_launching_scripts.yml b/detections/mshta_launching_scripts.yml deleted file mode 100644 index 6d916059a4..0000000000 --- a/detections/mshta_launching_scripts.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-08-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for the execution of "mshta.exe" with command-line - arguments that launch a script. The search will return the first time and last time - these command-line arguments were used for these executions, as well as the target - system, the user, process "mshta.exe" and its parent process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, parent_process_name - rule_description: Mshta.exe is seen to be executing scripts via the command-line - arguments - rule_title: Mshta.exe is executing scripts on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe - by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| - search (process=*vbscript* OR process=*javascript*)' - suppress: - suppress_fields: dest, process, parent_process_name - suppress_period: 86400s -eli5: Mshta.exe is a built-in Windows utility that can launch HTML files with .hta - extensions (HTML applications), javascript, or VBScript. The search detects this - behavior by looking for events where the process mshta.exe is executed with command-line - arguments that indicate that a script is invoked -entities: - - dest -how_to_implement: To successfully implement this search, you need to be ingesting - logs with the process name, parent process, and command-line executions from your - endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the - Sysmon TA. -id: b89919ed-fe5f-492c-b139-95dqb161039e -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Although unlikely, some legitimate applications may exhibit - this behavior, triggering a false positive. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Exploitation - mitre_attack: - - Execution - - Command-Line Interface - - Persistence - nist: - - PR.PT - - DE.CM -modification_date: '2018-12-03' -name: Detect mshta.exe running scripts in command-line arguments -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml b/detections/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml new file mode 100644 index 0000000000..c1be6e3e39 --- /dev/null +++ b/detections/multiple_okta_users_with_invalid_credentails_from_the_same_ip.yml @@ -0,0 +1,31 @@ +name: Multiple Okta Users With Invalid Credentails From The Same IP +id: 19cba45f-cad3-4032-8911-0c09e0444552 +version: 1 +date: '2020-04-01' +description: This search detects Okta login failures due to bad credentials for multiple + users originating from the same ip address. +how_to_implement: This search is specific to Okta and requires Okta logs are being + ingested in your Splunk deployment. +type: ESCU +author: Rico Valdez, Splunk +search: '`okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country + as country, client.geographicalContext.state as state, client.geographicalContext.city + as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users + values(user) as users by src_ip, displayMessage, outcome.reason, country, state, + city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | search distinct_users > 5| `multiple_okta_users_with_invalid_credentails_from_the_same_ip_filter` ' +known_false_positives: A single public IP address servicing multiple legitmate users + may trigger this search. In addition, the threshold of 5 distinct users may be too + low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise + the threshold or except specific IP adresses from triggering this search. +tags: + analytics_story: + - Suspicious Okta Activity + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - DE.CM + security_domain: access + asset_type: Infrastructure diff --git a/detections/netbackup_failed_backup.yml b/detections/netbackup_failed_backup.yml deleted file mode 100644 index 37a464c981..0000000000 --- a/detections/netbackup_failed_backup.yml +++ /dev/null @@ -1,86 +0,0 @@ -asset_type: Endpoint -baselines: - - id: b2178fed-592f-492b-b851-74161678aa56 - name: Monitor Unsuccessful Backups - type: splunk - - id: b4d0dfb2-2195-4f6e-93a3-48468ed9734e - name: Monitor Successful Backups - type: splunk -confidence: high -creation_date: '2017-06-15' -data_metadata: - data_source: - - Backup Systems - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup -description: This search gives you the hosts where a backup was attempted and then - failed. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The system $dest$ attempted a backup but encountered an - error. - rule_title: Failed backup attempt by $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 10 - schedule: - cron_schedule: 0 7 * * * - earliest_time: -24h@h - latest_time: -10m@m - search: sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, - MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` - | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, - signature - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search looks across the most recent backup events for each host, and returns - those messages that indicate there was a backup failure. -entities: - - dest -how_to_implement: To successfully implement this search you need to obtain data from - your backup solution, either from the backup logs on your endpoints or from a central - server responsible for performing the backups. If you do not use Netbackup, you - can modify this search for your specific backup solution. -id: a34aae96-ccf8-4aaa-952c-3ea21444444f -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72 - name: All backup logs for host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 10 - nist: - - PR.IP -modification_date: '2017-09-12' -name: Unsuccessful Netbackup backups -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/netbackup_no_backups_in_time_period.yml b/detections/netbackup_no_backups_in_time_period.yml deleted file mode 100644 index 07da95583a..0000000000 --- a/detections/netbackup_no_backups_in_time_period.yml +++ /dev/null @@ -1,83 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-06-15' -data_metadata: - data_source: - - Backup Systems - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup -description: This search returns a list of hosts that have not successfully completed - a backup in over a week. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The system $dest$ has not had a successful backup for an - extended period. - rule_title: Extended period of no successful backups by $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 10 - schedule: - cron_schedule: 0 0 1 * * - earliest_time: -7d@d - latest_time: -10m@m - search: sourcetype="netbackup_logs" MESSAGE="Disk/Partition backup completed - successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` - | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), - "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search finds all the successful backup messages in your logs, and then - looks for the most recent backup time for each system. It then identifies those - systems where the most recent successful backup time is over a week ago, and reports - on them. -entities: - - dest -how_to_implement: To successfully implement this search you need to first obtain data - from your backup solution, either from the backup logs on your hosts, or from a - central server responsible for performing the backups. If you do not use Netbackup, - you can modify this search for your backup solution. Depending on how often you - backup your systems, you may want to modify how far in the past to look for a successful - backup, other than the default of seven days. -id: a34aae96-ccf8-4aef-952c-3ea214444440 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72 - name: All backup logs for host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 10 - nist: - - PR.IP -modification_date: '2017-09-12' -name: Extended Period Without Successful Netbackup Backups -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/netsh_launching_process.yml b/detections/netsh_launching_process.yml deleted file mode 100644 index ece0d7e757..0000000000 --- a/detections/netsh_launching_process.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-01-04' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for processes launching netsh.exe to execute various - commands via the netsh command-line utility. Netsh.exe is a command-line scripting - utility that allows you to, either locally or remotely, display or modify the network - configuration of a computer that is currently running. Netsh can be used as a persistence - proxy technique to execute a helper .dll when netsh.exe is executed. In this search, - we are looking for processes spawned by netsh.exe that are executing commands via - the command line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, parent_process - rule_description: A process, $process$, is spawned by netsh.exe. It is highly - unlikely for netsh to have any child processes. - rule_title: Process spawned by netsh.exe detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*C:\\Windows\\System32\\netsh.exe*" by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name - | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process - suppress_period: 86400s -eli5: 'This search looks for all processes with the parent process "c:\Windows\System32\netsh.exe" and returns the process, the command line used to execute it, the host - name, and the user context under which it ran.' -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting logs - with the process name, command-line arguments, and parent processes from your endpoints. - If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -id: b89919ed-fe5f-492c-b139-95dbb162041e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: It is unusual for netsh.exe to have any child processes in - most environments. It makes sense to investigate the child process and verify whether - the process spawned is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Command-Line Interface - - Persistence - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-02' -name: Processes created by netsh -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/new_aws_console_login_by_user.yml b/detections/new_aws_console_login_by_user.yml deleted file mode 100644 index 8ba9d0620a..0000000000 --- a/detections/new_aws_console_login_by_user.yml +++ /dev/null @@ -1,97 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 - name: Previously seen users in CloudTrail - type: splunk - - id: 06c036e6-d6d7-4daa-bd76-411c3d356031 - name: Update previously seen users in CloudTrail - type: splunk -confidence: medium -creation_date: '2018-02-26' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events wherein a console login event - by a user was recorded within the last hour, then compares the event to a lookup - file of previously seen users (by ARN values) who have logged into the console. - The alert is fired if the user has logged into the console for the first time within - the last hour -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A new user has logged into the AWS console - rule_title: AWS Console Login by New User - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: 'sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn - as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user - | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) - as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= - relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously - Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" ' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: In this search, we query CloudTrail logs to look for events that indicate that - a user has attempted to log in to the AWS console and group the events using ARN - value. Using the `previously_seen_users_console_logins.csv` lookup file created - using the support search, we compare the ARN to all the previously seen users logging - into the AWS console. The `eval` and `if` functions determine whether the earliest - time we see this user ARN was seen within the last hour. The alert will be fired - only when a user is seen for first time in the last hour. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the "Previously seen users in CloudTrail" support search only once to - create a baseline of previously seen IAM users within the last 30 days. Run "Update - previously seen users in CloudTrail" hourly (or more frequently depending on how - often you run the detection searches) to refresh the baselines. -id: ada0f478-84a8-4641-a3f3-d82362dffd75 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - nist: - - DE.DP - - DE.AE -modification_date: '2018-04-30' -name: Detect new user AWS Console Login -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_aws_console_login_city_by_user.yml b/detections/new_aws_console_login_city_by_user.yml deleted file mode 100644 index f6e28251ef..0000000000 --- a/detections/new_aws_console_login_city_by_user.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 - name: Previously seen users in CloudTrail - type: splunk - - id: 06c036e6-d6d7-4daa-bd76-411c3d356031 - name: Update previously seen users in CloudTrail - type: splunk -confidence: medium -creation_date: '2018-04-24' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events wherein a console login event - by a user was recorded within the last hour, then compares the event to a lookup - file of previously seen users (by ARN values) who have logged into the console. - The alert is fired if the user has logged into the console for the first time within - the last hour -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A user has logged into the AWS console from a new city. - rule_title: AWS Console Login by User from New City - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) - as firstTime max(lastTime) as lastTime by user City | join user type=outer [| inputlookup - previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen - by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), - "@d"), "New City","Previously Seen City") | eval UserData=if(earliestseen - >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") - | where userStatus="New City" AND UserData="Old User" | `security_content_ctime(firstTime)` | - `security_content_ctime(lastTime)`| `security_content_ctime(earliestseen)` | table user City userStatus firstTime lastTime - earliestseen' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: In this search, we query CloudTrail logs to look for events that indicate that - a user has attempted to log in to the AWS console from a new city and group the - events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup - file created using the support search, we compare the ARN to all the previously - seen ARN and city combinations logging into the AWS console. The `eval` and `if` - functions determine whether the earliest time we see this user ARN was seen within - the last hour. The alert will be fired only when a user is seen for first time in - the last hour. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the "Previously seen users in CloudTrail" support search only once to - create a baseline of previously seen IAM users within the last 30 days. Run "Update - previously seen users in CloudTrail" hourly (or more frequently depending on how - often you run the detection searches) to refresh the baselines. -id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - nist: - - DE.DP - - DE.AE -modification_date: '2018-04-30' -name: Detect AWS Console Login by User from New City -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_aws_console_login_country_by_user.yml b/detections/new_aws_console_login_country_by_user.yml deleted file mode 100644 index 3e34a7eb5d..0000000000 --- a/detections/new_aws_console_login_country_by_user.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 - name: Previously seen users in CloudTrail - type: splunk - - id: 06c036e6-d6d7-4daa-bd76-411c3d356031 - name: Update previously seen users in CloudTrail - type: splunk -confidence: medium -creation_date: '2018-04-24' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events wherein a console login event - by a user was recorded within the last hour, then compares the event to a lookup - file of previously seen users (by ARN values) who have logged into the console. - The alert is fired if the user has logged into the console for the first time within - the last hour -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A user has logged into the AWS console from a new country. - rule_title: AWS Console Login by User from New Country - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) - as firstTime max(lastTime) as lastTime by user Country | join user type=outer [| - inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) - AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime - >= relative_time(now(), "@d"), "New Country","Previously Seen Country") | - eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), - "New User","Old User") | where userStatus="New Country" AND UserData="Old - User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`security_content_ctime(earliestseen)` | table user Country userStatus firstTime lastTime earliestseen' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: In this search, we query CloudTrail logs to look for events that indicate that - a user has attempted to log in to the AWS console from a new country and group the - events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup - file created using the support search, we compare the ARN to all the previously - seen ARN and country combinations logging into the AWS console. The `eval` and `if` - functions determine whether the earliest time we see this user ARN was seen within - the last hour. The alert will be fired only when a user is seen for first time in - the last hour. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the "Previously seen users in CloudTrail" support search only once to - create a baseline of previously seen IAM users within the last 30 days. Run "Update - previously seen users in CloudTrail" hourly (or more frequently depending on how - often you run the detection searches) to refresh the baselines. -id: 67bd3def-c41c-4bf6-837b-ae196b4257c6 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - nist: - - DE.DP - - DE.AE -modification_date: '2018-04-30' -name: Detect AWS Console Login by User from New Country -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_aws_console_login_region_by_user.yml b/detections/new_aws_console_login_region_by_user.yml deleted file mode 100644 index 5c32222880..0000000000 --- a/detections/new_aws_console_login_region_by_user.yml +++ /dev/null @@ -1,104 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd03 - name: Previously seen users in CloudTrail - type: splunk - - id: 06c036e6-d6d7-4daa-bd76-411c3d356031 - name: Update previously seen users in CloudTrail - type: splunk -confidence: medium -creation_date: '2018-04-24' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events wherein a console login event - by a user was recorded within the last hour, then compares the event to a lookup - file of previously seen users (by ARN values) who have logged into the console. - The alert is fired if the user has logged into the console for the first time within - the last hour -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A user has logged into the AWS console from a new region. - rule_title: AWS Console Login by User from New Region - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) - as firstTime max(lastTime) as lastTime by user Region | join user type=outer [| - inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) - AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime - >= relative_time(now(), "@d"), "New Region","Previously Seen Region") | eval - UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), - "New User","Old User") | where userStatus="New Region" AND UserData="Old User" - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `security_content_ctime(earliestseen)` | table user Region userStatus firstTime lastTime earliestseen' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: In this search, we query CloudTrail logs to look for events that indicate that - a user has attempted to log in to the AWS console from a new region and group the - events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup - file created using the support search, we compare the ARN to all the previously - seen ARN and region combinations logging into the AWS console. The `eval` and `if` - functions determine whether the earliest time we see this user ARN was seen within - the last hour. The alert will be fired only when a user is seen for first time in - the last hour. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Run the "Previously seen users in CloudTrail" support search only once to - create a baseline of previously seen IAM users within the last 30 days. Run "Update - previously seen users in CloudTrail" hourly (or more frequently depending on how - often you run the detection searches) to refresh the baselines. -id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk -known_false_positives: When a legitimate new user logins for the first time, this - activity will be detected. Check how old the account is and verify that the user - activity is legitimate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - nist: - - DE.DP - - DE.AE -modification_date: '2018-04-30' -name: Detect AWS Console Login by User from New Region -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_connections_to_routers.yml b/detections/new_connections_to_routers.yml deleted file mode 100644 index 749f124cfb..0000000000 --- a/detections/new_connections_to_routers.yml +++ /dev/null @@ -1,97 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-07-18' -data_metadata: - data_models: - - Authentication - data_source: - - Network Equipment - providing_technologies: - - Active Directory - - Palo Alto Firewall -description: The search queries the authentication logs for assets that are categorized - as routers in the ES Assets and Identity Framework, to identify connections that - have not been seen before in the last 30 days. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user - rule_description: This search detects new connections made to the router devices - at $dest$ - rule_title: Detected a New Router Login - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 0 * * * - earliest_time: -30d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) - as latest from datamodel=Authentication where Authentication.dest_category=router - by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= - relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| - `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")`' - suppress: - suppress_fields: dest,user - suppress_period: 86400s -eli5: Attackers will often attempt to compromise network devices such as routers for - a variety of nefarious purposes, including modifying VPN settings or re-routing - network traffic. Typically, only a relatively small number of user accounts log - into these devices on a regular basis. This search identifies 'new' connections - to your routers by checking to see if a similar login was made in the last 30 days. - Routers are identified by checking the IP address against those categorized as a - "router" in the ES assets and identity framework. -entities: - - dest -how_to_implement: To successfully implement this search, you must ensure the network - router devices are categorized as "router" in the Assets and identity table. You - must also populate the Authentication data model with logs related to users authenticating - to routing infrastructure. -id: 104658f4-afdc-499e-9719-17243rr826f1 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Legitimate router connections may appear as new connections -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 11 - kill_chain_phases: - - Actions on Objectives - nist: - - PR.PT - - PR.AC - - PR.IP -modification_date: '2017-09-12' -name: Detect New Login Attempts to Routers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_container_uploaded_to_aws_ecr.yml b/detections/new_container_uploaded_to_aws_ecr.yml new file mode 100644 index 0000000000..78800dfcdf --- /dev/null +++ b/detections/new_container_uploaded_to_aws_ecr.yml @@ -0,0 +1,26 @@ +name: New container uploaded to AWS ECR +id: f0f70b40-f7ad-489d-9905-23d149da8099 +version: 1 +date: '2020-02-20' +description: This searches show information on uploaded containers including source + user, image id, source IP user type, http user agent, region, first time, last time + of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. You must also install Cloud Infrastructure data model. Please also customize + the `container_implant_aws_detection_filter` macro to filter out the false positives. +type: ESCU +references: [] +author: Rod Soto, Rico Valdez, Splunk +search: '| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute + where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" + AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src + Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` + | `new_container_uploaded_to_aws_ecr_filter` ' +known_false_positives: Uploading container is a normal behavior from developers or + users with access to container registry. +tags: + analytics_story: + - Container Implantation Monitoring and Investigation + security_domain: threat + asset_type: AWS ECR container diff --git a/detections/new_open_s3_buckets.yml b/detections/new_open_s3_buckets.yml deleted file mode 100644 index 64684b7efd..0000000000 --- a/detections/new_open_s3_buckets.yml +++ /dev/null @@ -1,102 +0,0 @@ -asset_type: S3 Bucket -confidence: medium -creation_date: '2018-07-25' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user has created an open/public - S3 bucket. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: An open/public S3 bucket, $bucketName$, was created by $user$. - rule_title: Public S3 bucket $bucketName$ created by $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 70 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn - path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName - | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList - | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee - | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn - as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user - suppress: - suppress_fields: user,bucketName - suppress_period: 86400s -eli5: This search queries CloudTrail logs for events with S3 bucket access controls - given to the "All Users" group, which allows anyone in the world access to the resource. - This search generates a table displaying the time when the bucket was made public, - the permission of the S3 bucket, the bucket name, and the ARN of the user who created - the bucket. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail - inputs. The threshold value should be tuned to your environment. -id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242 - name: AWS S3 Bucket details via bucketName - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: While this search has no known false positives, it is possible - that an AWS admin has legitimately created a public bucket for a specific purpose. - That said, AWS strongly advises against granting full control to the "All Users" - group. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Initial Access - - Exfiltration - nist: - - PR.DS - - PR.AC - - DE.CM -modification_date: '2018-07-25' -name: Detect New Open S3 buckets -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/new_user_accounts.yml b/detections/new_user_accounts.yml deleted file mode 100644 index 7eabb40fcc..0000000000 --- a/detections/new_user_accounts.yml +++ /dev/null @@ -1,90 +0,0 @@ -asset_type: Domain Server -confidence: medium -creation_date: '2017-08-05' -data_metadata: - data_models: - - Identity_Management - data_source: - - Active Directory logs - providing_technologies: - - Active Directory -description: This detection search will help profile user accounts in your environment - by identifying newly created accounts that have been added to your network in the - past week. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: Using the identities lookup and macro from Enterprise Security - to identify (report) new users (6 month period) and temp users (3 months - until account expiration) - rule_title: Identify Temporary Users - risk: - risk_object: user - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 0 * * * - earliest_time: -24h@h - latest_time: -10m@m - search: '| from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, - "Accounts created in last week") | search empStatus="Accounts created in last - week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate - startDate' - suppress: - suppress_fields: identity - suppress_period: 86400s -eli5: Adversaries will often seek to create new user accounts as a means of maintaining - access to a target environment. Using this search, we identify accounts created - in the last week by comparing the start date in the Identity_Management data model - against the current time. -entities: - - user -how_to_implement: To successfully implement this search, you need to be populating - the Enterprise Security Identity_Management data model in the assets and identity - framework. -id: 475b9e27-17e4-46e2-b7e2-648221be3b89 -investigations: - - id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb - name: Get Logon Rights Modifications For User - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: If the Identity_Management data model is not updated regularly, - this search could give you false positive alerts. Please consider this and investigate - appropriately. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 16 - mitre_attack: - - Persistence - - Create Account - nist: - - PR.IP -modification_date: '2017-09-12' -name: Identify New User Accounts -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/no_win_updates_in_timeframe.yml b/detections/no_win_updates_in_timeframe.yml deleted file mode 100644 index 0b18b9d2c9..0000000000 --- a/detections/no_win_updates_in_timeframe.yml +++ /dev/null @@ -1,91 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-15' -data_metadata: - data_models: - - Updates - data_source: - - Windows Update Logs - providing_technologies: - - Microsoft Windows -description: This search looks for Windows endpoints that have not generated an event - indicating a successful Windows update in the last 60 days. Windows updates are - typically released monthly and applied shortly thereafter. An endpoint that has - not successfully applied an update in this time frame indicates the endpoint is - not regularly being patched for some reason. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, user - rule_description: The system $src$ has not generated a successful Windows - Update event in 60 days or more. - rule_title: No Windows updates in last 60 days on $src$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` max(_time) - as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft - Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest - as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product - as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), - 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as - "Last Update Time", | table Host, "Update Status", Product, "Last Update Time"' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: Keeping your systems up-to-date with the latest patches is an important step - in keeping your systems secured. For Windows endpoints, Microsoft typically releases - patches on the second Tuesday of every month. These patches contain fixes for vulnerabilities - in the system that could potentially be exploited by malicious actors. This search - checks for messages regarding Windows updates in the 'Update' data model. If a message - indicating a successful update has not been observed in 60 days, a notable event - will be generated. These systems should be checked to determine why it has not been - updated in that time frame. -entities: - - dest -how_to_implement: To successfully implement this search, it requires that the 'Update' - data model is being populated. This can be accomplished by ingesting Windows events - or the Windows Update log via a universal forwarder on the Windows endpoints you - wish to monitor. The Windows add-on should be also be installed and configured to - properly parse Windows events in Splunk. There may be other data sources which can - populate this data model, including vulnerability management systems. -id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 18 - nist: - - PR.PT - - PR.MA -modification_date: '2017-09-15' -name: No Windows Updates in a time frame -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/no_windows_updates_in_a_time_frame.yml b/detections/no_windows_updates_in_a_time_frame.yml new file mode 100644 index 0000000000..679b7cd72e --- /dev/null +++ b/detections/no_windows_updates_in_a_time_frame.yml @@ -0,0 +1,36 @@ +name: No Windows Updates in a time frame +id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f +version: 1 +date: '2017-09-15' +description: This search looks for Windows endpoints that have not generated an event + indicating a successful Windows update in the last 60 days. Windows updates are + typically released monthly and applied shortly thereafter. An endpoint that has + not successfully applied an update in this time frame indicates the endpoint is + not regularly being patched for some reason. +how_to_implement: To successfully implement this search, it requires that the 'Update' + data model is being populated. This can be accomplished by ingesting Windows events + or the Windows Update log via a universal forwarder on the Windows endpoints you + wish to monitor. The Windows add-on should be also be installed and configured to + properly parse Windows events in Splunk. There may be other data sources which can + populate this data model, including vulnerability management systems. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates + where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest + Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status + as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime + <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | + search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update + Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Monitor for Updates + cis20: + - CIS 18 + nist: + - PR.PT + - PR.MA + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/okta_account_lockout_events.yml b/detections/okta_account_lockout_events.yml new file mode 100644 index 0000000000..162e5d18a4 --- /dev/null +++ b/detections/okta_account_lockout_events.yml @@ -0,0 +1,26 @@ +name: Okta Account Lockout Events +id: 62b70968-a0a5-4724-8ac4-67871e6f544d +version: 1 +date: '2020-04-01' +description: Detect Okta user lockout events +how_to_implement: This search is specific to Okta and requires Okta logs are being + ingested in your Splunk deployment. +type: ESCU +author: Rico Valdez, Splunk +search: '`okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country + as country, client.geographicalContext.state as state, client.geographicalContext.city + as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` ' +known_false_positives: None. Account lockouts should be followed up on to determine + if the actual user was the one who caused the lockout, or if it was an unauthorized + actor. +tags: + analytics_story: + - Suspicious Okta Activity + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - DE.CM + security_domain: access + asset_type: Infrastructure diff --git a/detections/okta_failed_sso_attempt.yml b/detections/okta_failed_sso_attempt.yml deleted file mode 100644 index 57df64ca52..0000000000 --- a/detections/okta_failed_sso_attempt.yml +++ /dev/null @@ -1,69 +0,0 @@ -asset_type: Infrastructure -confidence: high -creation_date: '2020-04-01' -data_metadata: - data_eventtypes: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: Detect failed Okta SSO events -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: Okta user $user$ attempted to access unauthorized app - rule_title: Okta user SSO attempt to unauthorized app - risk: - risk_object: user - risk_object_type: - - user - risk_score: 50 - macros: - - okta_failed_sso_attempt_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: 'eventtype=okta_log displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`okta_failed_sso_attempt_filter`' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search looks for events that indicate a user attempted to access an app they did not have permissions to access. This could indicate attempts to access prohibited applications. Please leverage the `okta_failed_sso_attempt_filter` macro to filter out false positives -entities: - - user -how_to_implement: 'This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.' -id: 371a6545-2618-4032-ad84-93386b8698c5 -investigations: - - id: 24ff145d-4d16-420a-b047-480f2a51c403 - name: Investigate User Activities In Okta - type: splunk - - id: 420eb1b8-2992-45d1-80cf-0b1b2759524d - name: Investigate Okta Activity by App - type: splunk -known_false_positives: There may be a faulty config preventing legitmate users from accessing apps they should have access to. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 16 - mitre_attack: - - Valid Accounts - mitre_technique_id: - - T1078 - nist: - - DE.CM -modification_date: '2020-04-01' -name: Okta Failed SSO Attempts -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -security_domain: access -spec_version: 2 -type: splunk -version: "1" diff --git a/detections/okta_failed_sso_attempts.yml b/detections/okta_failed_sso_attempts.yml new file mode 100644 index 0000000000..9f1b9c370e --- /dev/null +++ b/detections/okta_failed_sso_attempts.yml @@ -0,0 +1,25 @@ +name: Okta Failed SSO Attempts +id: 371a6545-2618-4032-ad84-93386b8698c5 +version: 1 +date: '2020-04-01' +description: Detect failed Okta SSO events +how_to_implement: This search is specific to Okta and requires Okta logs are being + ingested in your Splunk deployment. +type: ESCU +author: Rico Valdez, Splunk +search: '`okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) + as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, + src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` ' +known_false_positives: There may be a faulty config preventing legitmate users from + accessing apps they should have access to. +tags: + analytics_story: + - Suspicious Okta Activity + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - DE.CM + security_domain: access + asset_type: Infrastructure diff --git a/detections/okta_multiple_users_with_auth_failures_from_ip.yml b/detections/okta_multiple_users_with_auth_failures_from_ip.yml deleted file mode 100644 index 854e39ddaf..0000000000 --- a/detections/okta_multiple_users_with_auth_failures_from_ip.yml +++ /dev/null @@ -1,66 +0,0 @@ -asset_type: Infrastructure -confidence: high -creation_date: '2020-04-01' -data_metadata: - data_eventtypes: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip - rule_description: Multiple Users Failing Authenticaiton From $src_ip$ - rule_title: Multiple Okta Users With Authentication Failures From a Single IP - risk: - risk_object: system - risk_object_type: - - system - risk_score: 50 - macros: - - okta_multiple_users_with_auth_failures_from_ip_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: 'eventtype=okta_log outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `okta_multiple_users_with_auth_failures_from_ip_filter`' - suppress: - suppress_fields: src_ip - suppress_period: 86400s -eli5: This search detects instances when there are more than 5 distinct users failing Okta logins due to invalid credentails from the same IP address. This may be indicative of attack techniques such as credential stuffing or password spraying, where an attacker attempts to login using common or found passwords and attempts to authenticate with them. -entities: - - src_ip -how_to_implement: 'This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.' -id: 19cba45f-cad3-4032-8911-0c09e0444552 -investigations: - - id: 56aae066-d619-477c-93e3-3fb83b2d23c3 - name: Investigate Okta Activity by IP Address - type: splunk -known_false_positives: A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 16 - mitre_attack: - - Valid Accounts - mitre_technique_id: - - T1078 - nist: - - DE.CM -modification_date: '2020-04-01' -name: Multiple Okta Users With Invalid Credentails From The Same IP -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -security_domain: access -spec_version: 2 -type: splunk -version: "1" diff --git a/detections/okta_user_lockouts.yml b/detections/okta_user_lockouts.yml deleted file mode 100644 index 549729a2bf..0000000000 --- a/detections/okta_user_lockouts.yml +++ /dev/null @@ -1,66 +0,0 @@ -asset_type: Infrastructure -confidence: high -creation_date: '2020-04-01' -data_metadata: - data_eventtypes: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: Detect Okta user lockout events -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: User $user$ locked out of Okta - too many attempts - rule_title: Okta user lockout events - risk: - risk_object: user - risk_object_type: - - user - risk_score: 50 - macros: - - okta_user_lockouts_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: 'eventtype=okta_log displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip |`okta_user_lockouts_filter`' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search detects when a user exceeds the maximum configured Okta login attempts and the account is subsequently locked out. This is often indicative of brtue force attempts against a user account. -entities: - - user -how_to_implement: 'This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.' -id: 62b70968-a0a5-4724-8ac4-67871e6f544d -investigations: - - id: 24ff145d-4d16-420a-b047-480f2a51c403 - name: Investigate User Activity In Okta - type: splunk -known_false_positives: None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 16 - mitre_attack: - - Valid Accounts - mitre_technique_id: - - T1078 - nist: - - DE.CM -modification_date: '2020-04-01' -name: Okta Account Lockout Events -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -security_domain: access -spec_version: 2 -type: splunk -version: "1" diff --git a/detections/okta_user_logins_from_multiple_cities.yml b/detections/okta_user_logins_from_multiple_cities.yml index d2f6af2418..416cf29617 100644 --- a/detections/okta_user_logins_from_multiple_cities.yml +++ b/detections/okta_user_logins_from_multiple_cities.yml @@ -1,66 +1,32 @@ -asset_type: Infrastructure -confidence: high -creation_date: '2020-04-01' -data_metadata: - data_eventtypes: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: This search detects logins from the same user from different states in a 24 hour period. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: User $user$ logged into Okta from different cities - rule_title: Okta user login from different cities - risk: - risk_object: user - risk_object_type: - - system - risk_score: 50 - macros: - - okta_user_logins_from_multiple_cities_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -24h@h - latest_time: -5m@m - search: 'eventtype=okta_log displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`okta_user_logins_from_multiple_cities_filter`| search locations > 1' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search detects users logging in from multiple states in the last 24 hours. This can be indicative of an attacker using compromised credentials to log in to Okta. The efficacy of this search is highly dependant on the mobility of the users using Okta. It is particularly useful in situations where users should explicitly *not* be travelling, such as during the COVID-19 pandemic. -entities: - - user -how_to_implement: 'This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.' -id: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8 -investigations: - - id: 24ff145d-4d16-420a-b047-480f2a51c403 - name: Investigate User Activity In Okta - type: splunk -known_false_positives: Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 16 - mitre_attack: - - Valid Accounts - mitre_technique_id: - - T1078 - nist: - - DE.CM -modification_date: '2020-04-01' name: Okta User Logins From Multiple Cities -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -security_domain: access -spec_version: 2 -type: splunk -version: "1" +id: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8 +version: 1 +date: '2020-04-01' +description: This search detects logins from the same user from different states in + a 24 hour period. +how_to_implement: This search is specific to Okta and requires Okta logs are being + ingested in your Splunk deployment. +type: ESCU +author: Rico Valdez, Splunk +search: '`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null + | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) + as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) + as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `okta_user_logins_from_multiple_cities_filter` | search locations > 1' +known_false_positives: Users in your enviornment may legitmately be travelling and + loggin in from different locations. This search is useful for those users that should + *not* be travelling for some reason, such as the COVID-19 pandemic. The search also + relies on the geographical information being populated in the Okta logs. It is also + possible that a connection from another region may be attributed to a login from + a remote VPN endpoint. +tags: + analytics_story: + - Suspicious Okta Activity + mitre_attack_id: + - T1078 + cis20: + - CIS 16 + nist: + - DE.CM + security_domain: access + asset_type: Infrastructure diff --git a/detections/open_redirect_in_splunk_web.yml b/detections/open_redirect_in_splunk_web.yml index 94a841893e..ed789747a8 100644 --- a/detections/open_redirect_in_splunk_web.yml +++ b/detections/open_redirect_in_splunk_web.yml @@ -1,82 +1,30 @@ -asset_type: Splunk Server -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: [] - data_source: - - Splunk - data_sourcetypes: - - splunk_web_access - providing_technologies: - - Splunk Enterprise +name: Open Redirect in Splunk Web +id: d199fb99-2312-451a-9daa-e5efa6ed76a7 +version: 1 +date: '2017-09-19' description: This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. -detect: - splunk: - correlation_rule: - notable: - nes_fields: host - rule_description: Search for exploitation of the Splunk Open Redirect Vulnerability - rule_title: Open Redirect in Splunk Web - risk: - risk_object: host - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: index=_internal sourcetype=splunk_web_access return_to="/%09/*" - suppress: - suppress_fields: host - suppress_period: 14400s -eli5: This search looks within Splunk's internal logs for evidence of CVE-2016-4859 - open redirect exploitation attempts. -entities: - - host how_to_implement: No extra steps needed to implement this search. -id: d199fb99-2312-451a-9daa-e5efa6ed76a7 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 4 - - CIS 18 - kill_chain_phases: - - Delivery - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - nist: - - ID.RA - - RS.MI - - PR.PT - - PR.AC - - PR.IP - - DE.CM -modification_date: '2017-09-19' -name: Open Redirect in Splunk Web -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +type: ESCU references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' +author: Bhavin Patel, Splunk +search: index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` +known_false_positives: None identified +tags: + analytics_story: + - Splunk Enterprise Vulnerability + kill_chain_phases: + - Delivery + cis20: + - CIS 3 + - CIS 4 + - CIS 18 + nist: + - ID.RA + - RS.MI + - PR.PT + - PR.AC + - PR.IP + - DE.CM + security_domain: network + asset_type: Splunk Server diff --git a/detections/osquery_pack___coldroot_detection.yml b/detections/osquery_pack___coldroot_detection.yml new file mode 100644 index 0000000000..1f690d96a4 --- /dev/null +++ b/detections/osquery_pack___coldroot_detection.yml @@ -0,0 +1,32 @@ +name: Osquery pack - ColdRoot detection +id: a6fffe5e-05c3-4c04-badc-887607fbb8dc +version: 1 +date: '2019-01-29' +description: This search looks for ColdRoot events from the osx-attacks osquery pack. +how_to_implement: In order to properly run this search, Splunk needs to ingest data + from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) + pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must + be deployed across your indexers and universal forwarders in order to have the osquery + data populate the Alerts data model +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd + OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | + bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter`' +known_false_positives: There are no known false positives. +tags: + analytics_story: + - ColdRoot MacOS RAT + kill_chain_phases: + - Installation + - Command and Control + cis20: + - CIS 4 + - CIS 8 + nist: + - DE.DP + - DE.CM + - PR.PT + security_domain: threat + asset_type: Endpoint diff --git a/detections/osx_coldroot_pack.yml b/detections/osx_coldroot_pack.yml deleted file mode 100644 index dc2cd0f89e..0000000000 --- a/detections/osx_coldroot_pack.yml +++ /dev/null @@ -1,99 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2019-01-29' -data_metadata: - data_models: - - Alerts - data_source: - - Endpoint - providing_technologies: - - OSquery -description: This search looks for ColdRoot events from the osx-attacks osquery pack. -detect: - splunk: - correlation_rule: - notable: - nes_fields: host, user - rule_description: Host $host$ generated an alert for the macOS RAT ColdRoot - rule_title: Osquery ColdRoot alert for $host$ - risk: - risk_object: host - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd - OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as - path | bucket _time span=30s | stats count(path) by _time, host, user, path' - suppress: - suppress_fields: host - suppress_period: 3600s -eli5: The search looks at the Alerts data model to identify those generated from - the osquery osx-attacks.conf pack, which search for the ColdRoot RAT. -entities: - - host -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) - pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must - be deployed across your indexers and universal forwarders in order to have the osquery - data populate the Alerts data model -id: a6fffe5e-05c3-4c04-badc-887607fbb8dc -investigations: - - id: 9df9ca9c-a02b-4f48-9eba-0bac55179050 - name: Investigate Network Traffic From src_ip - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: 2f5b960b-71df-49c0-affc-74992ce60e45 - name: Investigate Web Activity From src_ip - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There are no known false positives. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 4 - - CIS 8 - kill_chain_phases: - - Installation - - Command and Control - mitre_attack: - - Execution - - Persistence - - Command and Control - nist: - - DE.DP - - DE.CM - - PR.PT -modification_date: '2019-01-29' -name: Osquery pack - ColdRoot detection -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/osx_keyboard_taps.yml b/detections/osx_keyboard_taps.yml deleted file mode 100644 index 11b7e7f90c..0000000000 --- a/detections/osx_keyboard_taps.yml +++ /dev/null @@ -1,97 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2019-01-25' -data_metadata: - data_models: - - Alerts - data_source: - - Endpoint - providing_technologies: - - OSquery -description: This search looks for processes in an MacOS system that is tapping keyboard - events in MacOS, and essentially monitoring all keystrokes made by a user. This - is a common technique used by RATs to log keystrokes from a victim, although it - can also be used by legitimate processes like Siri to react on human input -detect: - splunk: - correlation_rule: - notable: - nes_fields: host, cmd, process_id - rule_description: Host $host$ has process $process_id$ tapping keyboard events - with command $cmd$ - rule_title: Host $host has process $process_id$ monitoring its keystrokes - risk: - risk_object: host - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps - | rename columns.cmdline as cmd, columns.name as process_name, columns.pid - as process_id| dedup host,process_name | table host,process_name, cmd, process_id' - suppress: - suppress_fields: host - suppress_period: 3600s -eli5: The search leverages Alerts generated from the osquery osx-attacks.conf pack - search `Keyboard_Event_Taps` to detect when a process is monitoring the keystrokes - of a machine, This is a common technique used by macOS remote access trojans to - log keystrokes from a machine -entities: - - host -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) - pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must - be deployed across your indexers and universal forwarders in order to have the osquery - data populate the Alerts data model. -id: 2a371608-331d-4034-ae2c-21dda8f1d0ec -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There might be some false positives as keyboard event taps - are used by processes like Siri and Zoom video chat, for some good examples of processes - to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) - comment. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 4 - - CIS 8 - kill_chain_phases: - - Command and Control - mitre_attack: - - Collection - nist: - - DE.DP -modification_date: '2019-01-25' -name: Processes Tapping Keyboard Events -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/outbound_smb_connections.yml b/detections/outbound_smb_connections.yml deleted file mode 100644 index 5db5723523..0000000000 --- a/detections/outbound_smb_connections.yml +++ /dev/null @@ -1,123 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-03-20' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream -description: This search looks for outbound SMB connections made by hosts within your - network to the Internet. SMB traffic is used for Windows file-sharing activity. - One of the techniques often used by attackers involves retrieving the credential - hash using an SMB request made to a compromised server controlled by the threat - actor. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest_ip - rule_description: Outbound SMB network traffic detected. - rule_title: Outbound SMB traffic from $src_ip$ to $dest_ip$ detected - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) - as latest values(All_Traffic.action) from datamodel=Network_Traffic where - All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 - OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip - All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 - AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(earliest)`| - `security_content_ctime(latest)` | `outbound_smb_connections_filter` ' - suppress: - suppress_fields: src_ip - suppress_period: 28800s -eli5: In this search, we are looking for the network connections that were not blocked - by the firewall and that are destined for destination port 139 or 445. We then filter - out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as - internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. - Since we are only looking for outbound traffic from the hosts made to the Internet, - we filter out traffic whose destination IP address is private. -entities: - - src_ip -how_to_implement: 'In order to run this search effectively, we highly recommend that - you leverage the Assets and Identity framework. It is important that you have good - understanding of how your network segments are designed, and be able to distinguish - internal from external address space. Add a category named `internal` to the CIDRs - that host the company''s assets in `assets_by_cidr.csv` lookup file, which is located - in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating - this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. - This search also requires you to be ingesting your network traffic and populating - the Network_Traffic data model' -id: 7f5fb3e1-4209-414-90db-0ec21b936378 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic - is legitimate, if the company's internal networks are not well-defined in the Assets - and Identity Framework. Categorize the internal CIDR blocks as `internal` in the - lookup file to avoid creating notable events for traffic destined to those CIDR - blocks. Any other network connection that is going out to the Internet should be - investigated and blocked. Best practices suggest preventing external communications - of all SMB versions and related protocols at the network boundary. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 12 - kill_chain_phases: - - Actions on Objectives - - Command and Control - mitre_attack: - - Commonly Used Port - - Credential Access - - Lateral Movement - mitre_technique_id: - - T1110 - - T1135 - - T1210 - nist: - - DE.CM -modification_date: '2020-01-22' -name: Detect Outbound SMB Traffic -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/outlook_writing_zip.yml b/detections/outlook_writing_zip.yml deleted file mode 100644 index 349d70a570..0000000000 --- a/detections/outlook_writing_zip.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: Endpoint -baselines: [] -confidence: high -creation_date: '2019-04-29' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for execution of process `outlook.exe` where the process - is writing a `.zip` file to the disk. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, file_name - rule_description: Outlook.exe is writing a zip file $file_name$ on $dest$ - rule_title: Outlook.exe is writing a zip file $file_name$ on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - macros: - - outlook_writing_zip_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime - FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe - OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id - Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name - Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | rename process_id as malicious_id| rename parent_process_id as outlook_id| - join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) - as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem - where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* - OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id - Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| - fields malicious_id outlook_id dest file_path file_name file_hash count file_id] - | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name - file_name file_path | where file_name != "" | `outlook_writing_zip_filter`' - - suppress: - suppress_fields: dest,file_name - suppress_period: 86400s -eli5: In this search, we are essentially trying to detect if outlook.exe is writing - a `.zip` file to the disk. The way this search would run is, it will execute the - the subsearch first which looks for all .zip files being written to the disk and - outputs a crucial field "process_id", that we use the main search to check if that - process\_id belongs to a process_name of outlook.exe. The search uses a join command - to essentially give you an end result of the first and last time that zip file was - written by outlook.exe, the dest and user logged on the system, the hash value and - the complete path to the zip file on disk -entities: - - dest -how_to_implement: You must be ingesting data that records filesystem and process activity - from your hosts to populate the Endpoint data model. This is typically populated - via endpoint detection-and-response products, such as Carbon Black, or endpoint - data sources, such as Sysmon. -id: a51bfe1a-94f0-4822-b1e4-16ae10145893 -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk -known_false_positives: It is not uncommon for outlook to write legitimate zip files - to the disk. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - - CIS 8 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Initial Access - - Spearphishing Attachment - nist: - - ID.AM - - PR.DS -modification_date: '2020-03-16' -name: Detect Oulook.exe writing a .zip file -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/overwrite_accessibility_binaries.yml b/detections/overwrite_accessibility_binaries.yml deleted file mode 100644 index aaad3c6666..0000000000 --- a/detections/overwrite_accessibility_binaries.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-12-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: Microsoft Windows contains accessibility features that can be launched - with a key combination before a user has logged in. An adversary can modify or replace - these programs so they can get a command prompt or backdoor without logging in to - the system. This search looks for modifications to these binaries. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file, $file_name$, was created in the default shim database - directory on $dest. - rule_title: Modification to accessibility binary, $file_path$, was detected - on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) - as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* - OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* - OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* - OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) - by Filesystem.file_name Filesystem.dest -| `drop_dm_object_name(Filesystem)` -| `security_content_ctime(lastTime)` -| `security_content_ctime(firstTime)`' - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: This search returns all the different accessibility binaries that have been - modified for each Windows host. -entities: - - dest -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint file-system data model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. -id: 13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Microsoft may provide updates to these binaries. Verify that - these changes do not correspond with your normal software update cycle. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Accessibility Features - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Overwriting Accessibility Binaries -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/overwriting_accessibility_binaries.yml b/detections/overwriting_accessibility_binaries.yml new file mode 100644 index 0000000000..db72c64b64 --- /dev/null +++ b/detections/overwriting_accessibility_binaries.yml @@ -0,0 +1,39 @@ +name: Overwriting Accessibility Binaries +id: 13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae +version: 3 +date: '2020-03-16' +description: Microsoft Windows contains accessibility features that can be launched + with a key combination before a user has logged in. An adversary can modify or replace + these programs so they can get a command prompt or backdoor without logging in to + the system. This search looks for modifications to these binaries. +how_to_implement: You must be ingesting data that records the filesystem activity + from your hosts to populate the Endpoint file-system data model node. If you are + using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which + you want to collect data. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) + as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* + OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* + OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* + OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) + by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`' +known_false_positives: Microsoft may provide updates to these binaries. Verify that + these changes do not correspond with your normal software update cycle. +tags: + analytics_story: + - Windows Privilege Escalation + mitre_attack_id: + - T1015 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/password_sharing.yml b/detections/password_sharing.yml deleted file mode 100644 index 23341f09f3..0000000000 --- a/detections/password_sharing.yml +++ /dev/null @@ -1,90 +0,0 @@ -asset_type: account -confidence: medium -creation_date: '2018-07-12' -data_metadata: - data_source: - - Network Communications - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search is used to identify user accounts that share a common password. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: This search is used to identify user accounts, $user$, that - share common passwords - rule_title: 'Web Fraud Detection: Password Sharing Across Accounts' - risk: - risk_object: user - risk_object_type: - - other - risk_score: 10 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | - rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data - "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames - values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 - suppress: - suppress_fields: user - suppress_period: 3600s -eli5: A common password across user accounts generally indicates that the users are - choosing poor passwords or that a fraudster has a common password across multiple - accounts embedded within a script. The search will extract the username and password - information from the form_data field, then calculate the number and values for usernames - that have the same passwords. Finally, it outputs the values where the unique usernames - sharing passwords are greater than 5 -entities: - - user -how_to_implement: We need to start with a dataset that allows us to see the values - of usernames and passwords that users are submitting to the website hosting the - Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized - or hashed value of a password is acceptable and certainly preferable to a clear-text - password. Common data sources used for this detection are customized Apache logs, - customized IIS, and Splunk Stream. -id: 31337a1a-53b9-4e05-96e9-55c934cb71d3 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89 - name: Get Web Session Information via session_id - type: splunk - - id: 5df39b3f-447d-4869-b673-8f45ad4616fe - name: Get Emails From Specific Sender - type: splunk -known_false_positives: As is common with many fraud-related searches, we are usually - looking to attribute risk or synthesize relevant context with loosely written detections - that simply detect anamoluous behavior. -maintainers: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -mappings: - cis20: - - CIS 16 - nist: - - DE.DP -modification_date: '2018-10-08' -name: Web Fraud - Password Sharing Across Accounts -original_authors: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -references: - - https://en.wikipedia.org/wiki/Session_ID - - https://en.wikipedia.org/wiki/Session_(computer_science) - - https://en.wikipedia.org/wiki/HTTP_cookie - - https://splunkbase.splunk.com/app/1809/ -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/path_interception_program.exe.yml b/detections/path_interception_program.exe.yml deleted file mode 100644 index 60faaca524..0000000000 --- a/detections/path_interception_program.exe.yml +++ /dev/null @@ -1,110 +0,0 @@ -asset_type: '' -confidence: medium -creation_date: '2017-11-16' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Tanium - - Ziften -description: 'The search is looking for the creation of program.exe in the C: drive. The - creation of this file in that location may be driven by a motive to perform path - interception.' -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_path, file_name - rule_description: 'A potentially malicious file program.exe was detected on - the C: drive. The creation of this file is often associated with a motive - to perform a path interception attack. ' - rule_title: Path Interception attempt discovered $dest$ via creation of program.exe - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_hash) - as file_hash values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem - where Filesystem.file_path="C:\\program.exe" by Filesystem.file_name | `drop_dm_object_name(Filesystem)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`' - suppress: - suppress_fields: dest, file_path, file_name - suppress_period: 86400s -eli5: 'This search queries the Endpoint file-system data model node to list out all - the values of destination machines, as well as the values of file hashes and file - paths that have the file "program.exe" in the C: drive. Path interception occurs - when an executable is placed in a specific path so that it is executed by an application - instead of by the intended target. In this case, applications vulnerable to path - interception (because of unquoted service paths with spaces in Windows registry) - allow attackers to execute maliciously crafted program.exes.' -entities: - - dest -how_to_implement: You must be ingesting data that records the file-system activity - from your hosts to populate the Endpoint file-system data model node. This is typically - populated via endpoint detection-and-response products, such as Carbon Black, or - other endpoint data sources, such as Sysmon. The data used for this search is typically - generated via logs that report file system reads and writes. -id: c77162d3-f93c-45cc-80c8-22f6v5264g9f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: 'It is unlikely that a normal user may create and place this - file in the C: drive. Confirm with the user.' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Privilege Escalation - - Persistence - nist: - - PR.PT - - DE.CM -modification_date: '2018-11-15' -name: Detect Path Interception By Creation Of program.exe -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/port_monitor_via_registry.yml b/detections/port_monitor_via_registry.yml deleted file mode 100644 index 82661bb377..0000000000 --- a/detections/port_monitor_via_registry.yml +++ /dev/null @@ -1,115 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-12-01' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: This search looks for registry activity associated with modifications - to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this - scenario, an attacker can load an arbitrary .dll into the print-monitor registry - by giving the full path name to the after.dll. The system will execute the .dll - with elevated (SYSTEM) permissions and will persist after reboot. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, registry_path - rule_description: A registry key associated with adding print monitors can - potentially be misused by giving it a path of a malicious .dll in the registry. - rule_title: Registry Key changes for Print Monitors detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND - Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by - Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path - Registry.action | `drop_dm_object_name(Registry)`' - suppress: - suppress_fields: dest, registry_path - suppress_period: 86400s -eli5: In this search, we look for modifications to registry keys used for adding print-monitor - entries on Microsoft platforms via the `registry_path` field in the endpoint data - model. It then provides the destination, command used to initiate the change, the - user who conducted this activity, the resource affected (registry_key_name), and - the entire path of the registry. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. - The data used for this search is typically generated via logs that report registry - modifications. -id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: You will encounter noise from legitimate print-monitor registry - entries. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Privilege Escalation - - Local Port Monitor - nist: - - PR.PT - - DE.CM - - PR.AC -modification_date: '2018-11-02' -name: Monitor Registry Keys for Print Monitors -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/powershell_get_sedebug.yml b/detections/powershell_get_sedebug.yml deleted file mode 100644 index 4d166dff37..0000000000 --- a/detections/powershell_get_sedebug.yml +++ /dev/null @@ -1,113 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-08-28' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search looks for PowerShell requesting privileges consistent with - credential dumping. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, dest - rule_description: Possible attempt at credential dumping via PowerShell was - detected on $dest$ by $user$. - rule_title: Event Code 4703 Specifying PowerShell Acquiring A Token with SeDebugPrivilege - Identified on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: eventtype=wineventlog_security signature_id=4703 Process_Name=*powershell.exe - | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" - | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) - as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs - as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)` - suppress: - suppress_fields: user, dest, process - suppress_period: 86400s -eli5: This search looks for Windows Event Code(signature_id) 4703 (token right adjusted), - where the process requesting the token change is PowerShell.exe and the requested - privilege is "SeDebugPrivilege". This is consistent with the use of PowerShell to - execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the - activity occurred, the process and associated id, the enabled privilege, and the - message in the event. -entities: - - dest -how_to_implement: 'You must be ingesting Windows Security logs. You must also enable - the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. - Additionally, this search requires you to enable your Group Management Audit Logs - in your Local Windows Security Policy and to be ingesting those logs. More information - on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. - Finally, please make sure that the local administrator group name is "Administrators" - to be able to look for the right group membership changes.' -id: 98917be2-bfc8-475a-8618-a9bb06575188 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: The activity may be legitimate. PowerShell is often used by - administrators to perform various tasks, and it's possible this event could be generated - in those cases. In these cases, false positives should be fairly obvious and you - may need to tweak the search to eliminate noise. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - nist: - - PR.IP - - PR.AC - - DE.CM -modification_date: '2019-02-27' -name: Detect Mimikatz Via PowerShell And EventCode 4703 -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/process_execution_via_wmi.yml b/detections/process_execution_via_wmi.yml new file mode 100644 index 0000000000..041418f51c --- /dev/null +++ b/detections/process_execution_via_wmi.yml @@ -0,0 +1,36 @@ +name: Process Execution via WMI +id: 24869767-8579-485d-9a4f-d9ddfd8f0cac +version: 3 +date: '2020-03-16' +description: This search looks for processes launched via WMI. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest + Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` ' +known_false_positives: Although unlikely, administrators may use wmi to execute commands + for legitimate purposes. +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/process_launching_netsh.yml b/detections/process_launching_netsh.yml deleted file mode 100644 index 41ca1b7bce..0000000000 --- a/detections/process_launching_netsh.yml +++ /dev/null @@ -1,118 +0,0 @@ -asset_type: Endpoint -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23 - name: Previously seen command line arguments - type: splunk - - id: df98763b-0b08-4281-8ef9-08db7ac572a9 - name: Baseline of SMB Traffic - MLTK - type: splunk -confidence: medium -creation_date: '2017-01-08' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for processes launching netsh.exe. Netsh is a command-line - scripting utility that allows you to, either locally or remotely, display or modify - the network configuration of a computer that is currently running. Netsh can be - used as a persistence proxy technique to execute a helper DLL when netsh.exe is - executed. In this search, we are looking for processes spawned by netsh.exe and - executing commands via the command line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, parent_process, cmdline - rule_description: 'A process detected on $dest$ is launching netsh.exe. ' - rule_title: Process launching netsh.exe detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process=netsh.exe by Processes.parent_process Processes.process_name - Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, parent_process - suppress_period: 86400s -eli5: This search looks for all the parent processes of netsh.exe and returns that - process, the command-line used to execute it, the host name, and the user context - under which it ran. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model -id: b89919ed-fe5f-492c-b139-95dbb162040e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Some VPN applications are known to launch netsh.exe. Outside - of these instances, it is unusual for an executable to launch netsh.exe and run - commands. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Command-Line Interface - - Persistence - - Defense Evasion - - Disabling Security Tools - nist: - - PR.PT - - DE.CM -modification_date: '2018-11-02' -name: Processes launching netsh -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/process_with_double_extension.yml b/detections/process_with_double_extension.yml deleted file mode 100644 index 6a081116e8..0000000000 --- a/detections/process_with_double_extension.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-01-26' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for processes launched from files that have double - extensions in the file name. This is typically done to obscure the "real" file extension - and make it appear as though the file being accessed is a data file, as opposed - to executable content. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process - rule_description: The system $dest$ executed a file with a double extension. - rule_title: Process With Multiple Extensions Launched on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe - OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process - = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe - by Processes.dest Processes.user Processes.process Processes.parent_process - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: This search uses the "Application State" data model to look for process names - with specific combinations of double extensions. Relatively straightforward, the - search looks for strings in the "process" field that match what you're looking for. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. -id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Persistence - - Change Default File Association - nist: - - DE.CM - - PR.PT - - PR.IP -modification_date: '2018-11-02' -name: Execution of File with Multiple Extensions -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/process_with_spaces_before_extension.yml b/detections/process_with_spaces_before_extension.yml deleted file mode 100644 index ca037e4e37..0000000000 --- a/detections/process_with_spaces_before_extension.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-01-26' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for processes launched from files with at least five - spaces in the name before the extension. This is typically done to obfuscate the - file extension by pushing it outside of the default view. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The system $dest$ executed a file with spaces before its - extension. - rule_title: Process $process$ with spaces before extension Launched on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process_path) as process_path - min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process = "* .*" by Processes.dest Processes.user Processes.process - Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`' - suppress: - suppress_fields: dest,process - suppress_period: 28800s -eli5: This search uses the endpoint data model to look for process names with at least - five spaces between the file name and its extension. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts to populate the endpoint data model - in the processes node. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -id: ab0353e6-a956-420b-b724-a8b4846d5d5a -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Persistence - - Change Default File Association - nist: - - DE.CM - - PR.PT - - PR.IP -modification_date: '2018-01-26' -name: Execution of File With Spaces Before Extension -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/processes_created_by_netsh.yml b/detections/processes_created_by_netsh.yml new file mode 100644 index 0000000000..bc4fffaad1 --- /dev/null +++ b/detections/processes_created_by_netsh.yml @@ -0,0 +1,40 @@ +name: Processes created by netsh +id: b89919ed-fe5f-492c-b139-95dbb162041e +version: 3 +date: '2020-03-02' +description: This search looks for processes launching netsh.exe to execute various + commands via the netsh command-line utility. Netsh.exe is a command-line scripting + utility that allows you to, either locally or remotely, display or modify the network + configuration of a computer that is currently running. Netsh can be used as a persistence + proxy technique to execute a helper .dll when netsh.exe is executed. In this search, + we are looking for processes spawned by netsh.exe that are executing commands via + the command line. +how_to_implement: To successfully implement this search, you must be ingesting logs + with the process name, command-line arguments, and parent processes from your endpoints. + If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.parent_process="*C:\\Windows\\System32\\netsh.exe*" by Processes.user + Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name + | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `processes_created_by_netsh_filter`' +known_false_positives: It is unusual for netsh.exe to have any child processes in + most environments. It makes sense to investigate the child process and verify whether + the process spawned is legitimate. +tags: + analytics_story: + - Netsh Abuse + mitre_attack_id: + - T1059 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/processes_launching_netsh.yml b/detections/processes_launching_netsh.yml new file mode 100644 index 0000000000..5bd7aa4cc1 --- /dev/null +++ b/detections/processes_launching_netsh.yml @@ -0,0 +1,40 @@ +name: Processes launching netsh +id: b89919ed-fe5f-492c-b139-95dbb162040e +version: 2 +date: '2018-11-02' +description: This search looks for processes launching netsh.exe. Netsh is a command-line + scripting utility that allows you to, either locally or remotely, display or modify + the network configuration of a computer that is currently running. Netsh can be + used as a persistence proxy technique to execute a helper DLL when netsh.exe is + executed. In this search, we are looking for processes spawned by netsh.exe and + executing commands via the command line. +how_to_implement: To successfully implement this search, you must be ingesting data + that records process activity from your hosts to populate the endpoint data model +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process=netsh.exe by Processes.parent_process Processes.process_name + Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + | `processes_launching_netsh_filter`' +known_false_positives: Some VPN applications are known to launch netsh.exe. Outside + of these instances, it is unusual for an executable to launch netsh.exe and run + commands. +tags: + analytics_story: + - Netsh Abuse + - Disabling Security Tools + - DHS Report TA18-074A + mitre_attack_id: + - T1059 + - T1089 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/processes_tapping_keyboard_events.yml b/detections/processes_tapping_keyboard_events.yml new file mode 100644 index 0000000000..7b6754c43a --- /dev/null +++ b/detections/processes_tapping_keyboard_events.yml @@ -0,0 +1,35 @@ +name: Processes Tapping Keyboard Events +id: 2a371608-331d-4034-ae2c-21dda8f1d0ec +version: 1 +date: '2019-01-25' +description: This search looks for processes in an MacOS system that is tapping keyboard + events in MacOS, and essentially monitoring all keystrokes made by a user. This + is a common technique used by RATs to log keystrokes from a victim, although it + can also be used by legitimate processes like Siri to react on human input +how_to_implement: In order to properly run this search, Splunk needs to ingest data + from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) + pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must + be deployed across your indexers and universal forwarders in order to have the osquery + data populate the Alerts data model. +type: ESCU +references: [] +author: Jose Hernandez, Splunk +search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps + | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| + dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`' +known_false_positives: There might be some false positives as keyboard event taps + are used by processes like Siri and Zoom video chat, for some good examples of processes + to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) + comment. +tags: + analytics_story: + - ColdRoot MacOS RAT + kill_chain_phases: + - Command and Control + cis20: + - CIS 4 + - CIS 8 + nist: + - DE.DP + security_domain: threat + asset_type: Endpoint diff --git a/detections/prohibited_allowed_through_fw.yml b/detections/prohibited_allowed_through_fw.yml deleted file mode 100644 index 41f72f4099..0000000000 --- a/detections/prohibited_allowed_through_fw.yml +++ /dev/null @@ -1,116 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-04-18' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Palo Alto Firewall - - Bro - - Splunk Stream -description: This search looks for network traffic defined by port and transport layer - protocol in the Enterprise Security lookup table "lookup_interesting_ports", that - is marked as prohibited, and has an associated 'allow' action in the Network_Traffic - data model. This could be indicative of a misconfigured network device. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest_ip - rule_description: This search looks for network traffic defined by port and - transport in the ES lookup table "lookup_interesting_ports", that is marked - as prohibited, and yet has an 'allow' action in the Network_Traffic data - model. This should help to identify areas where a network device is not - properly configured. - rule_title: Prohibited Network Traffic Allowed from $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed - by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action - | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port - OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`' - suppress: - suppress_fields: dest_ip,src_ip - suppress_period: 14400s -eli5: The search looks for traffic marked 'is_prohibited' in the Enterprise Security - lookup table 'interesting_ports_lookup', and then determines if any network devices - have an associated 'allow' action on that traffic by checking the Network_Traffic - data model. -entities: - - src_ip -how_to_implement: In order to properly run this search, Splunk needs to ingest data - from firewalls or other network control devices that mediate the traffic allowed - into an environment. This is necessary so that the search can identify an 'action' - taken on the traffic of interest. The search requires the Network_Traffic data model - be populated. -id: ce5a0962-849f-4720-a678-753fe6674479 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 9 - - CIS 12 - kill_chain_phases: - - Delivery - - Command and Control - mitre_attack: - - Command and Control - - Commonly Used Port - - Exfiltration - - Exfiltration Over Alternative Protocol - nist: - - DE.AE - - PR.AC -modification_date: '2017-09-11' -name: Prohibited Network Traffic Allowed -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/prohibited_apps_spawning_cmdprompt.yml b/detections/prohibited_apps_spawning_cmdprompt.yml deleted file mode 100644 index 7fd64215b3..0000000000 --- a/detections/prohibited_apps_spawning_cmdprompt.yml +++ /dev/null @@ -1,128 +0,0 @@ -asset_type: Endpoint -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd23 - name: Previously seen command line arguments - type: splunk - - id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459 - name: Baseline of Command Line Length - MLTK - type: splunk -confidence: medium -creation_date: '2017-10-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for executions of cmd.exe spawned by a process that - is often abused by attackers and that does not typically launch cmd.exe. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, parent_process - rule_description: A prohibited application from prohibited_apps_launching_cmd.csv - was leveraged to launch cmd.exe - rule_title: Prohibited application($parent_process_name$) used to launch cmd.exe - on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) - as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name - Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - |search [`prohibited_apps_launching_cmd`]' - suppress: - suppress_fields: dest, parent_process, user - suppress_period: 86400s -eli5: Obtaining access to the Command-Line Interface (CLI) is typically a primary - attacker goal. Once an attacker has obtained the ability to execute code on a target - system, they will often further manipulate the system via commands passed to the - CLI. It is also unusual for many applications to spawn a command shell during normal - operation, while it is often observed if an application has been compromised in - some way. As such, it is often beneficial to look for cmd.exe being executed by - processes that are often targeted for exploitation, or that would not spawn cmd.exe - in any other circumstances. A lookup file is provided to easily modify the processes - that are being watched for execution of cmd.exe. -entities: - - dest - - parent_process_name - - user -how_to_implement: You must be ingesting data that records process activity from your - hosts and populates the Endpoint data model with the resultant dataset. This search - includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list - of processes that should not be spawning cmd.exe. You can modify this lookup to - better suit your environment. -id: dcfd6b40-42f9-469d-a433-2e53f7486664 -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: There are circumstances where an application may legitimately - execute and interact with the Windows command-line interface. Investigate and modify - the lookup file, as appropriate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Exploitation - mitre_attack: - - Execution - - Command-Line Interface - nist: - - PR.PT - - DE.CM -modification_date: '2020-02-03' -name: Detect Prohibited Applications Spawning cmd.exe -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/prohibited_network_traffic_allowed.yml b/detections/prohibited_network_traffic_allowed.yml new file mode 100644 index 0000000000..abcba4ef5b --- /dev/null +++ b/detections/prohibited_network_traffic_allowed.yml @@ -0,0 +1,42 @@ +name: Prohibited Network Traffic Allowed +id: ce5a0962-849f-4720-a678-753fe6674479 +version: 1 +date: '2017-09-11' +description: This search looks for network traffic defined by port and transport layer + protocol in the Enterprise Security lookup table "lookup_interesting_ports", that + is marked as prohibited, and has an associated 'allow' action in the Network_Traffic + data model. This could be indicative of a misconfigured network device. +how_to_implement: In order to properly run this search, Splunk needs to ingest data + from firewalls or other network control devices that mediate the traffic allowed + into an environment. This is necessary so that the search can identify an 'action' + taken on the traffic of interest. The search requires the Network_Traffic data model + be populated. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by + All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action + | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port + OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Command and Control + mitre_attack_id: + - T1043 + - T1048 + kill_chain_phases: + - Delivery + - Command and Control + cis20: + - CIS 9 + - CIS 12 + nist: + - DE.AE + - PR.AC + security_domain: network + asset_type: Endpoint diff --git a/detections/prohibited_software_on_endpoint.yml b/detections/prohibited_software_on_endpoint.yml index 8addfad176..b402bc90bb 100644 --- a/detections/prohibited_software_on_endpoint.yml +++ b/detections/prohibited_software_on_endpoint.yml @@ -1,53 +1,9 @@ -asset_type: Endpoint -baselines: - - id: 251930a5-1451-4428-bb13-eed5775be0ce - name: Add Prohibited Processes to Enterprise Security - type: splunk -confidence: high -creation_date: '2017-06-26' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Prohibited Software On Endpoint +id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893 +version: 2 +date: '2019-10-11' description: This search looks for applications on the endpoint that you have marked as prohibited. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, user - rule_description: Prohibited software $process_name$ has been detected on - $dest$. - rule_title: Prohibited Software Detected On $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user - Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` - | `prohibited_softwares`' - suppress: - suppress_fields: dest,user - suppress_period: 86400s -eli5: This search returns the number of times, as well as the first and last time, - every process has run for each endpoint and user. It then displays only those processes - that you have marked as "prohibited" in the Enterprise Security "Interesting Processes" - table. -entities: - - dest how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response @@ -58,66 +14,27 @@ how_to_implement: To successfully implement this search, you must be ingesting d `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. -id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: d98675ed-da43-4a7e-96a7-eeca3232ba8e - name: Get Update Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 2 - kill_chain_phases: - - Installation - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - nist: - - ID.AM - - PR.DS -modification_date: '2019-10-11' -name: Prohibited Software On Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` + | `prohibited_softwares` | `prohibited_software_on_endpoint_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Monitor for Unauthorized Software + - 'Emotet Malware DHS Report TA18-201A ' + - SamSam Ransomware + kill_chain_phases: + - Installation + - Command and Control + - Actions on Objectives + cis20: + - CIS 2 + nist: + - ID.AM + - PR.DS + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/protocol_mismatch.yml b/detections/protocol_mismatch.yml deleted file mode 100644 index d319d33413..0000000000 --- a/detections/protocol_mismatch.yml +++ /dev/null @@ -1,117 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-04-18' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Palo Alto Firewall - - Bro - - Splunk Stream -description: This search looks for network traffic on common ports where a higher - layer protocol does not match the port that is being used. For example, this search - should identify cases where protocols other than HTTP are running on TCP port 80. - This can be used by attackers to circumvent firewall restrictions, or as an attempt - to hide malicious communications over ports and protocols that are typically allowed - and not well inspected. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest_ip, src_ip - rule_description: This search looks for network traffic on common ports where - the underlying protocol does not match the port being used. For example, - this search should identify cases where protocols other than HTTP are running - on port 80. This can be used by attackers to circumvent firewall restrictions, - or as an attempt to hide malicious communications in traffic that is typically - allowed and not well inspected. - rule_title: Protocol / Port Mismatch from $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) - OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 - OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl - NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp - NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, - All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `drop_dm_object_name("All_Traffic")`' - suppress: - suppress_fields: dest_ip, dest_port - suppress_period: 86400s -eli5: This search looks for instances in which the protocol observed is not consistent - with the port and transport protocol typically used for that protocol. For example, - looking for network traffic other than HTTP running over TCP port 80. Such behavior - could indicate a misconfiguration or a custom command and control protocol that - has been designed to look like ordinary web traffic. The search will also identify - if HTTP traffic is observed running on unexpected ports. This can be common in many - environments. -entities: - - src_ip -how_to_implement: Running this search properly requires a technology that can inspect - network traffic and identify common protocols. Technologies such as Bro and Palo - Alto Networks firewalls are two examples that will identify protocols via inspection, - and not just assume a specific protocol based on the transport protocol and ports. -id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 9 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Commonly Used Port - nist: - - DE.AE - - PR.AC -modification_date: '2017-09-11' -name: Protocol or Port Mismatch -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/protocol_or_port_mismatch.yml b/detections/protocol_or_port_mismatch.yml new file mode 100644 index 0000000000..0753a62007 --- /dev/null +++ b/detections/protocol_or_port_mismatch.yml @@ -0,0 +1,42 @@ +name: Protocol or Port Mismatch +id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3 +version: 1 +date: '2017-09-11' +description: This search looks for network traffic on common ports where a higher + layer protocol does not match the port that is being used. For example, this search + should identify cases where protocols other than HTTP are running on TCP port 80. + This can be used by attackers to circumvent firewall restrictions, or as an attempt + to hide malicious communications over ports and protocols that are typically allowed + and not well inspected. +how_to_implement: Running this search properly requires a technology that can inspect + network traffic and identify common protocols. Technologies such as Bro and Palo + Alto Networks firewalls are two examples that will identify protocols via inspection, + and not just assume a specific protocol based on the transport protocol and ports. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) + OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 + OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl + NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp + NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, + All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + cis20: + - CIS 9 + - CIS 12 + nist: + - DE.AE + - PR.AC + security_domain: network + asset_type: Endpoint diff --git a/detections/protocols_authenticating_in_cleartext.yml b/detections/protocols_authenticating_in_cleartext.yml deleted file mode 100644 index d57f19032c..0000000000 --- a/detections/protocols_authenticating_in_cleartext.yml +++ /dev/null @@ -1,101 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-03' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Splunk Stream - - Bro -description: This search looks for cleartext protocols at risk of leaking credentials. - Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous - FTP sessions. While some of these protocols can be used over SSL, they typically - run on different assigned ports in those cases. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, dest, user - rule_description: This search looks for the use of cleartext protocols that - are known to pass authentication information in the clear. The cleartext - credentials are typically passed at the beginning of the session. - rule_title: Possible credential leak over cleartext protocol - risk: - risk_object: user - risk_object_type: - - user - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND - (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" - OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby - All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | - `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search is checking for traffic on well-known ports that are associated - with protocols that pass authentication in cleartext. -entities: - - user -how_to_implement: This search requires you to be ingesting your network traffic, and - populating the Network_Traffic data model. -id: 6923cd64-17a0-453c-b945-81ac2d8c6db9 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Some networks may use kerberized FTP or telnet servers, however, - this is rare. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 9 - - CIS 14 - kill_chain_phases: - - Reconnaissance - - Actions on Objectives - mitre_attack: - - Credential Access - - Lateral Movement - - Collection - nist: - - PR.PT - - DE.AE - - PR.AC - - PR.DS -modification_date: '2017-09-15' -name: Protocols passing authentication in cleartext -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/protocols_passing_authentication_in_cleartext.yml b/detections/protocols_passing_authentication_in_cleartext.yml new file mode 100644 index 0000000000..cfd0b0f36f --- /dev/null +++ b/detections/protocols_passing_authentication_in_cleartext.yml @@ -0,0 +1,37 @@ +name: Protocols passing authentication in cleartext +id: 6923cd64-17a0-453c-b945-81ac2d8c6db9 +version: 1 +date: '2017-09-15' +description: This search looks for cleartext protocols at risk of leaking credentials. + Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous + FTP sessions. While some of these protocols can be used over SSL, they typically + run on different assigned ports in those cases. +how_to_implement: This search requires you to be ingesting your network traffic, and + populating the Network_Traffic data model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND + (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" + OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby All_Traffic.user + All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter`' +known_false_positives: Some networks may use kerberized FTP or telnet servers, however, + this is rare. +tags: + analytics_story: + - Use of Cleartext Protocols + kill_chain_phases: + - Reconnaissance + - Actions on Objectives + cis20: + - CIS 9 + - CIS 14 + nist: + - PR.PT + - DE.AE + - PR.AC + - PR.DS + security_domain: network + asset_type: Endpoint diff --git a/detections/psexec_accepteula.yml b/detections/psexec_accepteula.yml deleted file mode 100644 index 4855760d28..0000000000 --- a/detections/psexec_accepteula.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-03-28' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: This search looks for events where `PsExec.exe` is run with the `accepteula` - flag in the command line. PsExec is a built-in Windows utility that enables you - to execute processes on other systems. It is fully interactive for console applications. - This tool is widely used for launching interactive command prompts on remote systems. - Threat actors leverage this extensively for executing code on compromised systems. - If an attacker is running PsExec for the first time, they will be prompted to accept - the end-user license agreement (EULA), which can be passed as the argument `accepteula` - within the command line. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest,process_name - rule_description: The process pssxec.exe was run with the -accepteula flag - on $dest$ by $user$. - rule_title: PsExec executed with accepteula flag on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = PsExec.exe Processes.process = "*accepteula*" by - Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| - `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process_name - suppress_period: 86400s -eli5: In this search, we are looking for the PsExec process with `accepteula` on the - command line. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: b89919ed-fe5f-492c-b139-151xb162040e -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Administrators can leverage PsExec for accessing remote systems - and might pass `accepteula` as an argument if they are running this tool for the - first time. However, it is not likely that you'd see multiple occurrences of this - event on a machine -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Command-Line Interface - nist: - - PR.PT - - DE.CM -modification_date: '2019-02-26' -name: Detect PsExec With accepteula Flag -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/rare_executables_on_endpoint.yml b/detections/rare_executables_on_endpoint.yml deleted file mode 100644 index 82ce6103d9..0000000000 --- a/detections/rare_executables_on_endpoint.yml +++ /dev/null @@ -1,124 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-08-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search will return a table of rare processes, the names of the systems - running them, and the users who initiated each process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process - rule_description: The process $process$ was detected running on $dest. This - process is rare in your environment. - rule_title: Rare Process $process$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - macros: - - rare_executables_on_endpoint_filter - schedule: - cron_schedule: 10 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) - as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes - by Processes.process_name | rename Processes.process_name as process | rex - field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes - by Processes.process_name | rare Processes.process_name limit=30 | rename - Processes.process_name as process| `filter_rare_process_whitelist`| table - process ] | `rare_executables_on_endpoint_filter`' - suppress: - suppress_fields: dest, process - suppress_period: 86400s -eli5: This search first executes the subsearch and counts all of your processes to - determine the 10 most rare (the limit set is 10). It then filters out whitelisted - processes and outputs the first and last time a rare process was encountered, the - destination where the process is running, the count of occurrences, and the users - who initiated the processes. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records process activity from your hosts and populating the endpoint data model - with the resultant dataset. The macro `filter_rare_process_whitelist` searches two - lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` - and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, - add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from - the default lookup file, you will have to modify the macro itself to set the whitelist - value for that process to false. You can modify the limit parameter and search scheduling - to better suit your environment. -id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Some legitimate processes may be only rarely executed in your - environment. As these are identified, update `rare_process_whitelist_local.csv` - to filter them out of your search results. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 2 - - CIS 8 - kill_chain_phases: - - Installation - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - nist: - - ID.AM - - PR.PT - - PR.DS - - DE.CM -modification_date: '2020-03-16' -name: Detect Rare Executables -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '5.0' diff --git a/detections/reg_exe_manipulating_windows_services_registry_keys.yml b/detections/reg_exe_manipulating_windows_services_registry_keys.yml new file mode 100644 index 0000000000..d2aca8b976 --- /dev/null +++ b/detections/reg_exe_manipulating_windows_services_registry_keys.yml @@ -0,0 +1,47 @@ +name: Reg exe Manipulating Windows Services Registry Keys +id: 8470d755-0c13-45b3-bd63-387a373c10cf +version: 2 +date: '2019-03-01' +description: The search looks for reg.exe modifying registry keys that define Windows + services and their configurations. +how_to_implement: To successfully implement this search you need to be ingesting information + on registry changes that include the name of the process responsible for the changes + from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` + nodes. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) + as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes + where Processes.process_name = reg.exe by Processes.process_id Processes.dest | + `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | join [| tstats `security_content_summariesonly` values(Registry.registry_path) + as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\services\\*" + by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table + process_id dest registry_path] | `reg_exe_manipulating_windows_services_registry_keys_filter`' +known_false_positives: It is unusual for a service to be created or modified by directly + manipulating the registry. However, there may be legitimate instances of this behavior. + It is important to validate and investigate, as appropriate. +tags: + analytics_story: + - Windows Service Abuse + - Windows Persistence Techniques + mitre_attack_id: + - T1050 + - T1031 + - T1089 + kill_chain_phases: + - Installation + cis20: + - CIS 3 + - CIS 5 + - CIS 8 + nist: + - PR.IP + - PR.PT + - PR.AC + - PR.AT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/reg_exe_used_to_hide_files_directories_via_registry_keys.yml b/detections/reg_exe_used_to_hide_files_directories_via_registry_keys.yml new file mode 100644 index 0000000000..212df8aefb --- /dev/null +++ b/detections/reg_exe_used_to_hide_files_directories_via_registry_keys.yml @@ -0,0 +1,34 @@ +name: Reg exe used to hide files directories via registry keys +id: c77162d3-f93c-45cc-80c8-22f6b5264x9f +version: 2 +date: '2019-02-27' +description: The search looks for command-line arguments used to hide a file or directory + using the reg add command. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" + Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name + Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter`' +known_false_positives: None at the moment +tags: + analytics_story: + - Windows Defense Evasion Tactics + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/reg_manipulating_services.yml b/detections/reg_manipulating_services.yml deleted file mode 100644 index 65935cfa77..0000000000 --- a/detections/reg_manipulating_services.yml +++ /dev/null @@ -1,124 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: 2018-6-29 -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The search looks for reg.exe modifying registry keys that define Windows - services and their configurations. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process - rule_description: A registry key associated with Windows services was modified - via reg.exe on $dest$ by $user$. - rule_title: Modification of Windows Services Via Reg.exe on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) - as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name - = reg.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` - values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry - where Registry.registry_path="*\\services\\*" by Registry.process_id Registry.dest - | `drop_dm_object_name("Registry")` | table process_id dest registry_path]' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: This search looks for modifications to registry paths that specify the definition - and configuration of Windows services by reg.exe. Reg.exe is a Windows utility that - allows for manipulation of the registry via the command line. Malware often uses - the Windows services architecture to persist, hide in plain sight, and gain the - ability to interact with the Windows kernel. While it is common to modify the configuration - of Windows services (and new services may be created with software installs), the - use of reg.exe to create or modify a service configuration is unusual and a technique - commonly used by attackers. The search returns the count, the first time the activity - was seen, the last time activity was seen, the registry path that was modified, - the host where the modification took place, and the user that performed the modification. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting information - on registry changes that include the name of the process responsible for the changes - from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` - nodes. -id: 8470d755-0c13-45b3-bd63-387a373c10cf -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is unusual for a service to be created or modified by directly - manipulating the registry. However, there may be legitimate instances of this behavior. - It is important to validate and investigate, as appropriate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 8 - kill_chain_phases: - - Installation - mitre_attack: - - Persistence - - Privilege Escalation - - New Service - - Modify Existing Service - - Defense Evasion - - Disabling Security Tools - nist: - - PR.IP - - PR.PT - - PR.AC - - PR.AT - - DE.CM -modification_date: '2019-03-01' -name: Reg.exe Manipulating Windows Services Registry Keys -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/reg_used_to_hide_files.yml b/detections/reg_used_to_hide_files.yml deleted file mode 100644 index 401ed5a4a2..0000000000 --- a/detections/reg_used_to_hide_files.yml +++ /dev/null @@ -1,111 +0,0 @@ -asset_type: '' -confidence: medium -creation_date: '2017-10-27' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: The search looks for command-line arguments used to hide a file or directory - using the reg add command. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process - rule_description: Regedit.exe is used by attackers to hide malware files/directories - in windows environments via registry key settings. This rule detects command-line - arguments used to hide a file/directory - rule_title: 'Regedit.exe used to hide a file/directory on $dest$ ' - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" - Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name - Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)"' - suppress: - suppress_fields: dest,process - suppress_period: 86400s -eli5: Reg.exe is a binary native to Windows platform used to edit the registry hives - of the system. Attackers can leverage this binary to hide files by passing in arguments - that are used to hide the files. In the search, we first gather results with keywords, - add, Hidden, and REG_DWORD, that will be in the raw event and filter by process - and the command-line. We then leverage regular expressions on the command-line field - to look for /d value as 2 which is responsible for hiding a file or directory. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: c77162d3-f93c-45cc-80c8-22f6b5264x9f -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None at the moment -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Persistence - nist: - - DE.CM -modification_date: '2019-02-27' -name: Reg.exe used to hide files/directories via registry keys -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/registry_keys_for_creating_shim_databases.yml b/detections/registry_keys_for_creating_shim_databases.yml new file mode 100644 index 0000000000..62cf67b895 --- /dev/null +++ b/detections/registry_keys_for_creating_shim_databases.yml @@ -0,0 +1,36 @@ +name: Registry Keys for Creating SHIM Databases +id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb +version: 1 +date: '2017-09-15' +description: This search looks for registry activity associated with application compatibility + shims, which can be leveraged by attackers for various nefarious purposes. +how_to_implement: To successfully implement this search, you must populate the Change_Analysis + data model. This is typically populated via endpoint detection and response products, + such as Carbon Black or other endpoint data sources such as Sysmon. The data used + for this search is typically generated via logs that report reads and writes to + the registry. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry + AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") + by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, + All_Changes.object_path | `drop_dm_object_name("All_Changes")` | `registry_keys_for_creating_shim_databases_filter`' +known_false_positives: There are many legitimate applications that leverage shim databases + for compatibility purposes for legacy applications +tags: + analytics_story: + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + mitre_attack_id: + - T1138 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/registry_keys_used_for_persistence.yml b/detections/registry_keys_used_for_persistence.yml new file mode 100644 index 0000000000..f99bb6c5c0 --- /dev/null +++ b/detections/registry_keys_used_for_persistence.yml @@ -0,0 +1,49 @@ +name: Registry Keys Used For Persistence +id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b +version: 3 +date: '2017-10-10' +description: The search looks for modifications to registry keys that can be used + to launch an application or service at system startup. +how_to_implement: To successfully implement this search, you must be ingesting data + that records registry activity from your hosts to populate the endpoint data model + in the registry node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black or endpoint data sources, such as Sysmon. The data + used for this search is typically generated via logs that report reads and writes + to the registry. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) + as registry_key_name values(Registry.registry_path) as registry_path min(_time) + as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* + OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* + OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* + OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* + OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows + NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) + by Registry.dest , Registry.status, Registry.user | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter`' +known_false_positives: There are many legitimate applications that must execute on + system startup and will use these registry keys to accomplish that task. +tags: + analytics_story: + - Suspicious Windows Registry Activities + - Suspicious MSHTA Activity + - DHS Report TA18-074A + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + - Windows Persistence Techniques + - 'Emotet Malware DHS Report TA18-201A ' + mitre_attack_id: + - T1103 + - T1131 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + - DE.AE + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/registry_keys_used_for_privilege_escalation.yml b/detections/registry_keys_used_for_privilege_escalation.yml new file mode 100644 index 0000000000..c7f4ce6e95 --- /dev/null +++ b/detections/registry_keys_used_for_privilege_escalation.yml @@ -0,0 +1,41 @@ +name: Registry Keys Used For Privilege Escalation +id: c9f4b923-f8af-4155-b697-1354f5bcbc5e +version: 2 +date: '2020-03-02' +description: This search looks for modifications to registry keys that can be used + to elevate privileges. The registry keys under "Image File Execution Options" are + used to intercept calls to an executable and can be used to attach malicious binaries + to benign system binaries. +how_to_implement: To successfully implement this search, you must be ingesting data + that records registry activity from your hosts to populate the endpoint data model + in the registry node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data + used for this search is typically generated via logs that report reads and writes + to the registry. +type: ESCU +references: +- https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) + as registry_key_name values(Registry.registry_path) as registry_path min(_time) + as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows + NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag + OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | + `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter`' +known_false_positives: There are many legitimate applications that must execute upon + system startup and will use these registry keys to accomplish that task. +tags: + analytics_story: + - Windows Privilege Escalation + - Suspicious Windows Registry Activities + mitre_attack_id: + - T1015 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/registry_persistence.yml b/detections/registry_persistence.yml deleted file mode 100644 index 7e30236198..0000000000 --- a/detections/registry_persistence.yml +++ /dev/null @@ -1,115 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: The search looks for modifications to registry keys that can be used - to launch an application or service at system startup. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, registry_path - rule_description: A registry key that is used for persistence on Windows was - modified on $dest$ by $src_user$. - rule_title: Registry Key Associated With Persistence Modified on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as - registry_key_name values(Registry.registry_path) as registry_path min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where - (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* - OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* - OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* - OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* - OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File - Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) - by Registry.dest , Registry.status, Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `drop_dm_object_name(Registry)`' - suppress: - suppress_fields: dest,user,registry_path - suppress_period: 14400s -eli5: This search looks for specific registry paths that malware often uses to ensure - survivability and persistence on system startup. The search returns the count, the - first time the activity was seen, the last time the activity was seen, the registry - path that was modified, the host where the modification took place and the user - that performed the modification. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. -id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: There are many legitimate applications that must execute on - system startup and will use these registry keys to accomplish that task. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Registry Run Keys / Start Folder - - AppInit DLLs - - Authentication Package - nist: - - PR.PT - - DE.CM - - DE.AE -modification_date: '2017-10-10' -name: Registry Keys Used For Persistence -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/registry_privilege_escalation.yml b/detections/registry_privilege_escalation.yml deleted file mode 100644 index 7876961fed..0000000000 --- a/detections/registry_privilege_escalation.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-12-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: This search looks for modifications to registry keys that can be used - to elevate privileges. The registry keys under "Image File Execution Options" are - used to intercept calls to an executable and can be used to attach malicious binaries - to benign system binaries. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, registry_path - rule_description: A registry key used for privilege escalation was modified - on $dest$ by $user$. - rule_title: Registry Key Associated With Privilege Escalation Modified on - $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`' - suppress: - suppress_fields: dest, user, registry_path - suppress_period: 14400s -eli5: This search looks for specific registry paths that malware often uses to elevate - privileges. The search returns the count, the first time the activity was seen, - the last time the activity was seen, the registry path that was modified, the host - where the modification took place, and the user who performed the modification. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting data - that records registry activity from your hosts to populate the endpoint data model - in the registry node. This is typically populated via endpoint detection-and-response - products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data - used for this search is typically generated via logs that report reads and writes - to the registry. -id: c9f4b923-f8af-4155-b697-1354f5bcbc5e -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: There are many legitimate applications that must execute upon - system startup and will use these registry keys to accomplish that task. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Privilege Escalation - - Persistence - - Accessibility Features - mitre_technique_id: - - T1183 - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-02' -name: Registry Keys Used For Privilege Escalation -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: - - https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/ -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/remote_desktop_bruteforce.yml b/detections/remote_desktop_bruteforce.yml deleted file mode 100644 index 09035f4273..0000000000 --- a/detections/remote_desktop_bruteforce.yml +++ /dev/null @@ -1,104 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-12-14' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream -description: This search looks for RDP application network traffic and filters any - source/destination pair generating more than twice the standard deviation of the - average traffic. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, src - rule_description: Remote-desktop traffic detected from $src$ to $dest$. This - activity is consistent with a brute-force attack. - rule_title: Bruteforce Remote Desktop Network Traffic detected from $src$ - to $dest$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src - All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev - avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src - AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count - avg p50 stdev' - suppress: - suppress_fields: dest,src - suppress_period: 28800s -eli5: This search monitors for abnormal amounts of remote-desktop (RDP) traffic from - a source to a destination that may be indicative of a brute-force attack. It does - this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, - using twice the standard deviation of all source-to-destination connections. If - any tuple is within more than two standard deviations of all other usual RDP traffic - flows, it is indicative of a brute-force attack. -entities: - - src -how_to_implement: You must ensure that your network traffic data is populating the - Network_Traffic data model. -id: a98727cc-286b-4ff2-b898-41df64695923 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc - name: Investigate Successful Remote Desktop Authentications - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: RDP gateways may have unusually high amounts of traffic from - all other hosts' RDP applications in the network. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 12 - - CIS 9 - - CIS 16 - kill_chain_phases: - - Reconnaissance - - Delivery - mitre_attack: - - Credential Access - - Remote Desktop Protocol - - Lateral Movement - nist: - - DE.AE - - PR.AC - - PR.IP -modification_date: '2018-12-14' -name: Remote Desktop Network Bruteforce -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/remote_desktop_network_bruteforce.yml b/detections/remote_desktop_network_bruteforce.yml new file mode 100644 index 0000000000..bcc3efb903 --- /dev/null +++ b/detections/remote_desktop_network_bruteforce.yml @@ -0,0 +1,38 @@ +name: Remote Desktop Network Bruteforce +id: a98727cc-286b-4ff2-b898-41df64695923 +version: 1 +date: '2018-12-14' +description: This search looks for RDP application network traffic and filters any + source/destination pair generating more than twice the standard deviation of the + average traffic. +how_to_implement: You must ensure that your network traffic data is populating the + Network_Traffic data model. +type: ESCU +references: [] +author: Jose Hernandez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src + All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) + AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src + AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 + stdev | `remote_desktop_network_bruteforce_filter`' +known_false_positives: RDP gateways may have unusually high amounts of traffic from + all other hosts' RDP applications in the network. +tags: + analytics_story: + - SamSam Ransomware + mitre_attack_id: + - T1076 + kill_chain_phases: + - Reconnaissance + - Delivery + cis20: + - CIS 12 + - CIS 9 + - CIS 16 + nist: + - DE.AE + - PR.AC + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/remote_desktop_network_traffic.yml b/detections/remote_desktop_network_traffic.yml index 3a2b2913c0..60065ade2e 100644 --- a/detections/remote_desktop_network_traffic.yml +++ b/detections/remote_desktop_network_traffic.yml @@ -1,53 +1,12 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream +name: Remote Desktop Network Traffic +id: 272b8407-842d-4b3d-bead-a704584003d3 +version: 2 +date: '2020-01-10' description: This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, src - rule_description: Remote Desktop Traffic detected between $src$ and $dest$. These - two systems typically do not communicate with RDP - rule_title: Uncommon Remote Desktop Network Traffic between $src$ and $dest$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 50 - macros: - - remote_desktop_network_traffic_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND - All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source - by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `remote_desktop_network_traffic_filter` ' - suppress: - suppress_fields: dest,src - suppress_period: 28800s -eli5: This search finds systems that do not commonly communicate use remote desktop. It - does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" - category applied to that system. Categories are applied to systems using the Assets - and Identity framework. -entities: - - src how_to_implement: To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating @@ -57,67 +16,31 @@ how_to_implement: To successfully implement this search you need to identify sys to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -id: 272b8407-842d-4b3d-bead-a704584003d3 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc - name: Investigate Successful Remote Desktop Authentications - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Remote Desktop may be used legitimately by users on the network. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 9 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - - Remote Desktop Protocol - mitre_technique_id: - - T1076 - nist: - - DE.AE - - PR.AC - - PR.IP -modification_date: '2020-01-10' -name: Remote Desktop Network Traffic -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND + All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source + by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` + | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` ' +known_false_positives: Remote Desktop may be used legitimately by users on the network. +tags: + analytics_story: + - SamSam Ransomware + - Hidden Cobra Malware + - Lateral Movement + mitre_attack_id: + - T1076 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 9 + - CIS 16 + nist: + - DE.AE + - PR.AC + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/remote_desktop_process_running_on_system.yml b/detections/remote_desktop_process_running_on_system.yml index 211667b562..180761501a 100644 --- a/detections/remote_desktop_process_running_on_system.yml +++ b/detections/remote_desktop_process_running_on_system.yml @@ -1,53 +1,11 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Remote Desktop Process Running On System +id: f5939373-8054-40ad-8c64-cec478a22a4a +version: 4 +date: '2020-01-13' description: This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: The system $dest$ is running the remote desktop process, - mstsc.exe. This system does not commonly run this application. - rule_title: Remote Desktop Process Running On $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 40 - macros: - - remote_desktop_process_running_on_system_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe - AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user - Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` ' - suppress: - suppress_fields: dest,user - suppress_period: 28800s -eli5: This search finds systems that do not commonly use remote desktop, but which - begin using it. It filters out all systems that have the "common_rdp_source" category - applied. Categories are applied to systems using the Assets and Identity framework. -entities: - - dest how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly @@ -56,64 +14,30 @@ how_to_implement: To successfully implement this search, you must be ingesting d to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. -id: f5939373-8054-40ad-8c64-cec478a22a4a -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Remote Desktop may be used legitimately by users on the network. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 9 - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - - Remote Desktop Protocol - mitre_technique_id: - - T1076 - nist: - - DE.AE - - PR.AC - - PR.IP -modification_date: '2020-01-13' -name: Remote Desktop Process Running On System -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe + AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user + Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` ' +known_false_positives: Remote Desktop may be used legitimately by users on the network. +tags: + analytics_story: + - Hidden Cobra Malware + - Lateral Movement + mitre_attack_id: + - T1076 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 9 + - CIS 16 + nist: + - DE.AE + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/remote_process_instantiation_via_wmi.yml b/detections/remote_process_instantiation_via_wmi.yml new file mode 100644 index 0000000000..46e3a48eaa --- /dev/null +++ b/detections/remote_process_instantiation_via_wmi.yml @@ -0,0 +1,41 @@ +name: Remote Process Instantiation via WMI +id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da +version: 3 +date: '2019-02-27' +description: This search looks for wmic.exe being launched with parameters to spawn + a process on a remote system. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" + Processes.process="*call*" Processes.process="*create*" by Processes.process_name + Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`' +known_false_positives: The wmic.exe utility is a benign Windows application. It may + be used legitimately by Administrators with these parameters for remote system administration, + but it's relatively uncommon. +tags: + analytics_story: + - Ransomware + - Suspicious WMI Use + mitre_attack_id: + - T1047 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/remote_registry_key_modifications.yml b/detections/remote_registry_key_modifications.yml new file mode 100644 index 0000000000..3f7f0f342c --- /dev/null +++ b/detections/remote_registry_key_modifications.yml @@ -0,0 +1,34 @@ +name: Remote Registry Key modifications +id: c9f4b923-f8af-4155-b697-1354f5dcbc5e +version: 3 +date: '2020-03-02' +description: This search monitors for remote modifications to registry keys. +how_to_implement: To successfully implement this search, you must populate the `Endpoint` + data model. This is typically populated via endpoint detection-and-response products, + such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for + this search is typically generated via logs that report reads and writes to the + registry. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) + as registry_key_name values(Registry.registry_path) as registry_path min(_time) + as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by + Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter`' +known_false_positives: This technique may be legitimately used by administrators to + modify remote registries, so it's important to filter these events out. +tags: + analytics_story: + - Windows Defense Evasion Tactics + - Suspicious Windows Registry Activities + - Windows Persistence Techniques + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/remote_registry_modifications.yml b/detections/remote_registry_modifications.yml deleted file mode 100644 index d2d8fd0b88..0000000000 --- a/detections/remote_registry_modifications.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-05-31' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: This search monitors for remote modifications to registry keys. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user - rule_description: A registry key was modified remotely using the machine $dest$ - by $user$. - rule_title: Remote Registry Key Modification detection on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as - registry_key_name values(Registry.registry_path) as registry_path min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by - Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `drop_dm_object_name(Registry)`' - suppress: - suppress_fields: dest, user,registry_path - suppress_period: 14400s -eli5: This search looks for modifications made to the Windows registry from remote - locations using reg.exe—a tool used to create/update/delete/modify Windows - registry keys. It is accomplished through specifying the machine names in the registry - path, by entering double backslashes, followed by a computer name. In this search, - we look for registry changes where the registry path contains the name of a remote - computer. The search returns the number of times the remote server has been accessed, - the first and last times the activity occurred, the name of the modified registry - path, the host on which the modification took place, and the name of the user that - performed the modification. -entities: - - dest -how_to_implement: To successfully implement this search, you must populate the `Endpoint` - data model. This is typically populated via endpoint detection-and-response products, - such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for - this search is typically generated via logs that report reads and writes to the - registry. -id: c9f4b923-f8af-4155-b697-1354f5dcbc5e -investigations: - - id: fecf2918-670d-4f1c-872b-3d7317a41xf9 - name: Get Registry Activities - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: This technique may be legitimately used by administrators to - modify remote registries, so it's important to filter these events out. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Persistence - - Lateral Movement - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-02' -name: Remote Registry Key modifications -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/remote_wmi_command_attempt.yml b/detections/remote_wmi_command_attempt.yml index b77e560ef6..b7b1e87801 100644 --- a/detections/remote_wmi_command_attempt.yml +++ b/detections/remote_wmi_command_attempt.yml @@ -1,110 +1,39 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-01-13' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Remote WMI Command Attempt +id: 272df6de-61f1-4784-877c-1fbc3e2d0838 +version: 2 +date: '2018-12-03' description: This search looks for wmic.exe being launched with parameters to operate on remote systems. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest,user,process_name - rule_description: This search looks for wmic.exe being launched with parameters - to operate on remote systems. - rule_title: Endpoint - Remote WMI command attempt - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 50 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND - Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name - Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,user,process_name - suppress_period: 28800s -eli5: Many a times, attackers leverage native Windows utilities that are designed - to help administrators better manage their systems, infrastructure, and auditing, - but are instead leveraged for malicious purposes. In this case, we are looking for - instances of wmic.exe being run with various parameters that are not typically used - by administrators. -entities: - - dest how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: 272df6de-61f1-4784-877c-1fbc3e2d0838 -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND + Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name + Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter`' known_false_positives: Administrators may use this legitimately to gather info from remote systems. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2018-12-03' -name: Remote WMI Command Attempt -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/remote_wmi_process_instantiation.yml b/detections/remote_wmi_process_instantiation.yml deleted file mode 100644 index 5d8eb5780a..0000000000 --- a/detections/remote_wmi_process_instantiation.yml +++ /dev/null @@ -1,113 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-01-13' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - Sysmon - - Tanium - - Ziften -description: This search looks for wmic.exe being launched with parameters to spawn - a process on a remote system. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for wmic.exe being launched with parameters - to spawn a process on a remote system. - rule_title: Remote process instantiation via WMI on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" - Processes.process="*call*" Processes.process="*create*" by Processes.process_name - Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,user,process - suppress_period: 28800s -eli5: Attackers are increasingly abusing native Windows utilities such as wmic.exe - as a means to "live off the land", and avoid introducing new executables to the - target system. In this search, we are looking for instances of wmic.exe being run - with various parameters that are not typically used by administrators. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk -known_false_positives: The wmic.exe utility is a benign Windows application. It may - be used legitimately by Administrators with these parameters for remote system administration, - but it's relatively uncommon. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2019-02-27' -name: Remote Process Instantiation via WMI -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/rundll_loading_dll_by_ordinal.yml b/detections/rundll_loading_dll_by_ordinal.yml index 537107a7f1..c1916199b0 100644 --- a/detections/rundll_loading_dll_by_ordinal.yml +++ b/detections/rundll_loading_dll_by_ordinal.yml @@ -1,106 +1,40 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-08-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: RunDLL Loading DLL By Ordinal +id: 6c135f8d-5e60-454e-80b7-c56eed739833 +version: 2 +date: '2019-02-27' description: This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for DLLs under %AppData% being loaded - by rundll32.exe that are calling the exported function at ordinal 2. Calling - exported functions by ordinal is not as common as calling by exported name. There - was a bug fixed in IDAPro on 2016-08-08 that would not display functions - with no names. Calling functions by ordinal would overcome the lack of - name and make it harder for analyst to reverse engineer. - rule_title: Endpoint - Suspicious RunDLL usage - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = rundll32.exe Processes.process="*AppData*" Processes.process="*,#2" - by Processes.process_name Processes.parent_process_name Processes.dest Processes.user - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,user - suppress_period: 28800s -eli5: This search looks for rundll32.exe being run, loading a DLL out of a directory - or subdirectory of AppData, and specifying the function at ordinal 2 be run. -entities: - - dest how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: 6c135f8d-5e60-454e-80b7-c56eed739833 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = rundll32.exe Processes.process="*AppData*" Processes.process="*,#2" + by Processes.process_name Processes.parent_process_name Processes.dest Processes.user + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `rundll_loading_dll_by_ordinal_filter`' known_false_positives: While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 +tags: + analytics_story: + - Unusual Processes + mitre_attack_id: + - T1085 kill_chain_phases: - - Installation - mitre_attack: - - Execution - - Rundll32 + - Installation + cis20: + - CIS 8 nist: - - PR.PT - - DE.CM -modification_date: '2019-02-27' -name: RunDLL Loading DLL By Ordinal -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/s3_access_from_new_ip.yml b/detections/s3_access_from_new_ip.yml deleted file mode 100644 index 28cdd3904e..0000000000 --- a/detections/s3_access_from_new_ip.yml +++ /dev/null @@ -1,115 +0,0 @@ -asset_type: S3 Bucket -baselines: - - id: fc0edc15-fq2c-48b0-9f6f-63qa1281fd03 - name: Previously seen S3 bucket access by remote IP - type: splunk -confidence: low -creation_date: '2018-06-25' -data_metadata: - data_source: - - AWS S3 Access logs - data_sourcetypes: - - aws:s3:accesslogs - providing_technologies: - - AWS -description: This search looks at S3 bucket-access logs and detects new or previously - unseen remote IP addresses that have successfully accessed an S3 bucket. -detect: - splunk: - correlation_rule: - notable: - nes_fields: bucket_name, src_ip - rule_description: A remote IP, $src_ip$, has made a successful connection - with an S3 $bucket_name$. - rule_title: S3 bucket $bucketName$ was accessed by a new $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 5 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs - http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime - by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv - | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name - remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval - newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| - iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name - src_ip City Country operation request_uri - suppress: - suppress_fields: bucket_name, src_ip - suppress_period: 86400s -eli5: 'Here the subsearch executes first and returns all successful S3 bucket-access - attempts (HTTP code "200") within the last hour. It groups the results by the earliest - and latest times it has seen a remote IP accessing a particular bucket. It appends - this information to the historical data from the lookup file and then recalculates - the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. - Next, it returns only those remote IP addresses that have first been seen accessing - a specific bucket within the past hour. This is combined with the main search to - return the time, bucket name, source IP, city, and country operations performed, - as well as the requested URI of the resource ' -entities: - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access - logs' inputs. This search works best when you run the "Previously Seen S3 Bucket - Access by Remote IP" support search once to create a history of previously seen - remote IPs and bucket names. -id: 2a9b80d3-6340-4345-b5ad-291bq3d0daq4 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242 - name: AWS S3 Bucket details via bucketName - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: S3 buckets can be accessed from any IP, as long as it can make - a successful connection. This will be a false postive, since the search is looking - for a new IP within the past hour -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - - CIS 14 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Exfiltration - nist: - - PR.DS - - PR.AC - - DE.CM -modification_date: '2018-06-28' -name: Detect S3 access from a new IP -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/samsam_payload_extensions.yml b/detections/samsam_payload_extensions.yml deleted file mode 100644 index a77a2e5ee1..0000000000 --- a/detections/samsam_payload_extensions.yml +++ /dev/null @@ -1,104 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-12-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: The search looks for file writes with extensions consistent with a SamSam - ransomware attack. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file with an extension associated with SamSam ransomware - was written on $dest$. - rule_title: File with known SamSam extension detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) - as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | - `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| - rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin - OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos - OR file_extension=.keyxml' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at file modifications across your hosts and creates notable - events when it identifies files with extensions associated with the SamSam ransomware, - including `.stubbin`, `.berkshire`, `.satoshi`, `.sophos`, or `.keyxml`. Files with - these extensions have been observed in SamSam attacks consisting of payload data - or keying material. -entities: - - dest -how_to_implement: You must be ingesting data that records file-system activity from - your hosts to populate the Endpoint file-system data-model node. If you are using - Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you - want to collect data. -id: 02c6cfc2-ae66-4735-bfc7-6291da834cbf -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Because these extensions are not typically used in normal operations, - you should investigate all results. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Installation - mitre_attack: [] - nist: - - PR.PT - - DE.CM -modification_date: '2018-12-14' -name: File with Samsam Extension -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/samsam_test_file_write.yml b/detections/samsam_test_file_write.yml index 4ec58941c1..4e7b278bb3 100644 --- a/detections/samsam_test_file_write.yml +++ b/detections/samsam_test_file_write.yml @@ -1,101 +1,31 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-12-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon +name: Samsam Test File Write +id: 69c12d59-d951-431e-ab77-ec426b8d65e6 +version: 1 +date: '2018-12-14' description: The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file named "test.txt," which is indicative of a SamSam - ransomware attack, was written to system32 on $dest$. - rule_title: File consistent with SamSam probes detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) - as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt - by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` - | `security_content_ctime(firstTime)`' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at file modifications across your hosts and monitors for a - file named "test.txt" written to "windows\system32". This file is copied to potential - targets during SamSam ransomware attacks to test the attacker's ability to access - remote systems. If the file is successfully copied to the system, the system is - added to a list of targets on which to deploy ransomware. -entities: - - dest how_to_implement: You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -id: 69c12d59-d951-431e-ab77-ec426b8d65e6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: No false positives have been identified. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Delivery - mitre_attack: [] - nist: - - PR.PT - - DE.CM -modification_date: '2018-12-14' -name: Samsam Test File Write -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) + as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt + by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`' +known_false_positives: No false positives have been identified. +tags: + analytics_story: + - SamSam Ransomware + kill_chain_phases: + - Delivery + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/sc_exe_manipulating_windows_services.yml b/detections/sc_exe_manipulating_windows_services.yml new file mode 100644 index 0000000000..78563ec4a4 --- /dev/null +++ b/detections/sc_exe_manipulating_windows_services.yml @@ -0,0 +1,48 @@ +name: Sc exe Manipulating Windows Services +id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d +version: 2 +date: '2019-02-27' +description: This search looks for arguments to sc.exe indicating the creation or + modification of a Windows service. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* + config *") by Processes.process_name Processes.parent_process_name Processes.dest + Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter`' +known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However, + there may be legitimate instances of this behavior. It is important to validate + and investigate as appropriate. +tags: + analytics_story: + - Windows Service Abuse + - DHS Report TA18-074A + - Orangeworm Attack Group + - Windows Persistence Techniques + - Disabling Security Tools + mitre_attack_id: + - T1050 + - T1031 + - T1089 + kill_chain_phases: + - Installation + cis20: + - CIS 3 + - CIS 5 + - CIS 8 + nist: + - PR.IP + - PR.PT + - PR.AC + - PR.AT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/sc_manipulating_services.yml b/detections/sc_manipulating_services.yml deleted file mode 100644 index 6da35b9a50..0000000000 --- a/detections/sc_manipulating_services.yml +++ /dev/null @@ -1,118 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-11-03' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for arguments to sc.exe indicating the creation or - modification of a Windows service. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for arguments to sc.exe indicating the - creation or modification of a Windows service. - rule_title: Sc.exe Manipulating Windows Services on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* - config *") by Processes.process_name Processes.parent_process_name Processes.dest - Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: This search looks for the execution of sc.exe with parameters that indicate - the utility is being used to create a new Windows service, or modify an existing - one. Attackers often create a new service to host their malicious code, or they - may take a non-critical service or one that is disabled, and modify it to point - to their malware and enable the service if necessary. It is unusual for a service - to be created or modified using the sc.exe utility. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However, - there may be legitimate instances of this behavior. It is important to validate - and investigate as appropriate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 8 - kill_chain_phases: - - Installation - mitre_attack: - - Persistence - - Privilege Escalation - - New Service - - Modify Existing Service - - Defense Evasion - - Disabling Security Tools - nist: - - PR.IP - - PR.PT - - PR.AC - - PR.AT - - DE.CM -modification_date: '2019-02-27' -name: Sc.exe Manipulating Windows Services -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/scheduled_task_name_used_by_dragonfly_threat_actors.yml b/detections/scheduled_task_name_used_by_dragonfly_threat_actors.yml new file mode 100644 index 0000000000..43886afd77 --- /dev/null +++ b/detections/scheduled_task_name_used_by_dragonfly_threat_actors.yml @@ -0,0 +1,34 @@ +name: Scheduled Task Name Used by Dragonfly Threat Actors +id: d5af132c-7c17-439c-9d31-13d55340f36c +version: 3 +date: '2020-03-16' +description: This search looks for flags passed to schtasks.exe on the command-line + that indicate a task name associated with the Dragonfly threat actor was created + or deleted. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe by + Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | search (process=*delete* OR process=*create*) process=*reset* | `scheduled_task_name_used_by_dragonfly_threat_actors_filter` ' +known_false_positives: No known false positives +tags: + analytics_story: + - DHS Report TA18-074A + mitre_attack_id: + - T1053 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/scheduled_tasks_used_in_badrabbit_ransomware.yml b/detections/scheduled_tasks_used_in_badrabbit_ransomware.yml new file mode 100644 index 0000000000..7e9a7e8efe --- /dev/null +++ b/detections/scheduled_tasks_used_in_badrabbit_ransomware.yml @@ -0,0 +1,35 @@ +name: Scheduled tasks used in BadRabbit ransomware +id: 1297fb80-f42a-4b4a-9c8b-78c066437cf6 +version: 2 +date: '2019-02-28' +description: This search looks for flags passed to schtasks.exe on the command-line + that indicate that task names related to the execution of Bad Rabbit ransomware + were created or deleted. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes + where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= + "*delete*") by Processes.parent_process Processes.process_name Processes.user | + `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter`' +known_false_positives: No known false positives +tags: + analytics_story: + - Ransomware + mitre_attack_id: + - T1053 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/schtasks_forced_reboot.yml b/detections/schtasks_forced_reboot.yml deleted file mode 100644 index 63be186546..0000000000 --- a/detections/schtasks_forced_reboot.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-11-03' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for flags passed to schtasks.exe on the command-line - that indicate that a forced reboot of system is scheduled. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for flags passed to schtasks.exe on the - command-line that indicate a job is scheduled to force a reboot - rule_title: Schtasks used for scheduling a force reboot - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -5h@h - latest_time: -1h@h - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = schtasks.exe Processes.process="*shutdown*" Processes.process="*/r*" - Processes.process="*/f*" by Processes.process_name Processes.parent_process_name - Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: The search looks for execution of schtasks.exe with parameters that indicate - a task is being scheduled that would cause a forced reboot on the host. Schtasks.exe - is a native windows program that is used to schedule tasks on local or remote systems. - Attackers often leverage this capability to schedule the execution of commands or - establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting logs - with both the process name and command-line from your endpoints. If you are using - Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Administrators may create jobs on systems forcing reboots to - perform updates, maintenance, etc. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Execution - - Scheduled Task - nist: - - PR.IP -modification_date: '2019-02-27' -name: Schtasks used for forcing a reboot -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/schtasks_scheduling_job_on_remote_system.yml b/detections/schtasks_scheduling_job_on_remote_system.yml index ad2661b6f7..5c614d7dd1 100644 --- a/detections/schtasks_scheduling_job_on_remote_system.yml +++ b/detections/schtasks_scheduling_job_on_remote_system.yml @@ -1,111 +1,36 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Schtasks scheduling job on remote system +id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6 +version: 3 +date: '2020-01-10' description: This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for flags passed to schtasks.exe on the - command-line that indicate a job is being scheduled on a remote system. - rule_title: Schtasks scheduling job on remote system - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - schtasks_scheduling_job_on_remote_system_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime - from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" - Processes.process="* /s *" by Processes.process_name Processes.process Processes.parent_process_name - Processes.dest Processes.user | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `schtasks_scheduling_job_on_remote_system_filter`' - suppress: - suppress_fields: dest,process - suppress_period: 28800s -eli5: The search looks for execution of schtasks.exe with parameters that indicate - a task is being scheduled on a remote host. Schtasks.exe is a native windows program - that is used to schedule tasks on local or remote systems. Attackers often leverage - this capability to schedule the execution of commands or malicious executables on - remote systems. -entities: - - dest how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe + Processes.process="*/create*" Processes.process="* /s *" by Processes.process_name + Processes.process Processes.parent_process_name Processes.dest Processes.user | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `schtasks_scheduling_job_on_remote_system_filter`' known_false_positives: Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 +tags: + analytics_story: + - Lateral Movement + mitre_attack_id: + - T1053 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Privilege Escalation - - Execution - - Scheduled Task - mitre_technique_id: - - T1053 + - Actions on Objectives + cis20: + - CIS 3 nist: - - PR.IP -modification_date: '2020-01-10' -name: Schtasks scheduling job on remote system -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/schtasks_used_for_forcing_a_reboot.yml b/detections/schtasks_used_for_forcing_a_reboot.yml new file mode 100644 index 0000000000..f4df93b592 --- /dev/null +++ b/detections/schtasks_used_for_forcing_a_reboot.yml @@ -0,0 +1,34 @@ +name: Schtasks used for forcing a reboot +id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6 +version: 2 +date: '2019-02-27' +description: This search looks for flags passed to schtasks.exe on the command-line + that indicate that a forced reboot of system is scheduled. +how_to_implement: To successfully implement this search you need to be ingesting logs + with both the process name and command-line from your endpoints. If you are using + Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = schtasks.exe Processes.process="*shutdown*" Processes.process="*/r*" + Processes.process="*/f*" by Processes.process_name Processes.parent_process_name + Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter`' +known_false_positives: Administrators may create jobs on systems forcing reboots to + perform updates, maintenance, etc. +tags: + analytics_story: + - Windows Persistence Techniques + - Ransomware + mitre_attack_id: + - T1053 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/script_execution_via_wmi.yml b/detections/script_execution_via_wmi.yml new file mode 100644 index 0000000000..e76aec7810 --- /dev/null +++ b/detections/script_execution_via_wmi.yml @@ -0,0 +1,36 @@ +name: Script Execution via WMI +id: aa73f80d-d728-4077-b226-81ea0c8be589 +version: 3 +date: '2020-03-16' +description: This search looks for scripts launched via WMI. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes + where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | + `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| + `script_execution_via_wmi_filter` ' +known_false_positives: Although unlikely, administrators may use wmi to launch scripts + for legitimate purposes. +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/shim_database_file_creation.yml b/detections/shim_database_file_creation.yml index 651ce27ba6..6f88619b37 100644 --- a/detections/shim_database_file_creation.yml +++ b/detections/shim_database_file_creation.yml @@ -1,108 +1,37 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-10-03' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon +name: Shim Database File Creation +id: 6e4c4588-ba2f-42fa-97e6-9f6f548eaa33 +version: 2 +date: '2018-11-02' description: This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A file, $file_name$, was created in the default shim database - directory on $dest. - rule_title: Shim database file created on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) - as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime - max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\AppPatch\Custom* - by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - |`drop_dm_object_name(Filesystem)`' - suppress: - suppress_fields: dest - suppress_period: 14400s -eli5: This search looks for files being created in `Windows\AppPatch\Custom and Windows\AppPatch\Custom64`, - the location where shim databases are installed. It will return all the files created, - as well as the time of creation for the first and last file for each endpoint. -entities: - - dest how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -id: 6e4c4588-ba2f-42fa-97e6-9f6f548eaa33 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Filesystem.action) + values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) + as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\AppPatch\Custom* + by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`' known_false_positives: Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 +tags: + analytics_story: + - Windows Persistence Techniques + mitre_attack_id: + - T1138 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Application Shimming + - Actions on Objectives + cis20: + - CIS 8 nist: - - DE.CM -modification_date: '2018-11-02' -name: Shim Database File Creation -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/shim_database_installation.yml b/detections/shim_database_installation.yml deleted file mode 100644 index e5ba23da80..0000000000 --- a/detections/shim_database_installation.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-10-03' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search detects the process execution and arguments required to silently - create a shim database. The sdbinst.exe application is used to install shim database - files (.sdb). A shim is a small library which transparently intercepts an API, changes - the parameters passed, handles the operation itself, or redirects the operation - elsewhere. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: The system $dest$ had a shim database installed. - rule_title: Shim Database Installation on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = sdbinst.exe Processes.process="*-p*" Processes.process="*-q*" - by Processes.process_name Processes.parent_process_name Processes.dest Processes.user - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,user - suppress_period: 14400s -eli5: This search looks for the execution of sdbinst.exe with command-line arguments - of -q and -p. The -q option performs a silent installation with no visible window, - status, or warning information. The -p option allows the shim database to contain - patches. It will return the count, the first time, and the last time these command-line - arguments were seen on each endpoint and by each user. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: 404620de-46d8-48b6-90cc-8a8d7b0876a3 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Persistence - - Application Shimming - nist: - - DE.CM -modification_date: '2019-03-01' -name: Shim Database Installation With Suspicious Parameters -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/shim_database_installation_with_suspicious_parameters.yml b/detections/shim_database_installation_with_suspicious_parameters.yml new file mode 100644 index 0000000000..dfdffc0195 --- /dev/null +++ b/detections/shim_database_installation_with_suspicious_parameters.yml @@ -0,0 +1,37 @@ +name: Shim Database Installation With Suspicious Parameters +id: 404620de-46d8-48b6-90cc-8a8d7b0876a3 +version: 2 +date: '2019-03-01' +description: This search detects the process execution and arguments required to silently + create a shim database. The sdbinst.exe application is used to install shim database + files (.sdb). A shim is a small library which transparently intercepts an API, changes + the parameters passed, handles the operation itself, or redirects the operation + elsewhere. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = sdbinst.exe Processes.process="*-p*" Processes.process="*-q*" + by Processes.process_name Processes.parent_process_name Processes.dest Processes.user + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `shim_database_installation_with_suspicious_parameters_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Windows Persistence Techniques + mitre_attack_id: + - T1138 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/short_lived_accounts.yml b/detections/short_lived_accounts.yml deleted file mode 100644 index d169f9b149..0000000000 --- a/detections/short_lived_accounts.yml +++ /dev/null @@ -1,99 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-01-05' -data_metadata: - data_models: - - Change - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search detects accounts that were created and deleted in a short - time period. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: The account $user$ was created and deleted in a short amount - of time. - rule_title: Short lived account $user$ on $dest - risk: - risk_object: user - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 0,4,8,12,16,20 * * * - earliest_time: -245m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id - count min(_time) as firstTime max(_time) as lastTime from datamodel=Change - where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h - All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` - | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 - | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest - result_id' - suppress: - suppress_fields: user - suppress_period: 86400s -eli5: This search looks for Windows Event Logs 4720 (account creation) and 4726 (account - deletion) and determines if they happen for the same user within 4 hours of each - other. It will report the user and machine that reported the events and the time - it first and last saw this activity. -entities: - - user -how_to_implement: 'This search requires you to have enabled your Group Management - Audit Logs in your Local Windows Security Policy and be ingesting those logs. More - information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/' -id: b25f6f62-0782-43c1-b403-083231ffd97d -investigations: - - id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb - name: Get Logon Rights Modifications For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505 - name: Get Logon Rights Modifications For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is possible that an administrator created and deleted an - account in a short time period. Verifying activity with an administrator is advised. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 16 - mitre_attack: - - Persistence - - Create Account - nist: - - PR.IP -modification_date: '2018-01-05' -name: Short Lived Windows Accounts -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: access -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/short_lived_windows_accounts.yml b/detections/short_lived_windows_accounts.yml new file mode 100644 index 0000000000..ed209295c9 --- /dev/null +++ b/detections/short_lived_windows_accounts.yml @@ -0,0 +1,32 @@ +name: Short Lived Windows Accounts +id: b25f6f62-0782-43c1-b403-083231ffd97d +version: 1 +date: '2018-01-05' +description: This search detects accounts that were created and deleted in a short + time period. +how_to_implement: 'This search requires you to have enabled your Group Management + Audit Logs in your Local Windows Security Policy and be ingesting those logs. More + information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/' +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as + result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change + where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h + All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 + | transaction user connected=false maxspan=240m | table firstTime lastTime count + user dest result_id | `short_lived_windows_accounts_filter`' +known_false_positives: It is possible that an administrator created and deleted an + account in a short time period. Verifying activity with an administrator is advised. +tags: + analytics_story: + - Account Monitoring and Controls + mitre_attack_id: + - T1136 + cis20: + - CIS 16 + nist: + - PR.IP + security_domain: access + asset_type: Windows diff --git a/detections/single_letter_executables.yml b/detections/single_letter_executables.yml deleted file mode 100644 index 74a63bb0cd..0000000000 --- a/detections/single_letter_executables.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-03-22' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for process names that consist only of a single letter. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, user - rule_description: A process with a single letter, $process_name$ was detected - on $dest$ - rule_title: Single-letter executable $process_name$ on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, - Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), - endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 - AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name' - suppress: - suppress_fields: dest, user - suppress_period: 86400s -eli5: This search returns all the processes for each endpoint and user and filters - out any process that isn't 5 characters long and ends with .exe. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: a4214f0b-e01c-41bc-8cc4-d2b71e3056b4 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Single-letter executables are not always malicious. Investigate - this activity with your normal incident-response process. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 2 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - ID.AM - - PR.DS -modification_date: '2019-04-01' -name: Single Letter Process On Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/single_letter_process_on_endpoint.yml b/detections/single_letter_process_on_endpoint.yml new file mode 100644 index 0000000000..0083898f8e --- /dev/null +++ b/detections/single_letter_process_on_endpoint.yml @@ -0,0 +1,34 @@ +name: Single Letter Process On Endpoint +id: a4214f0b-e01c-41bc-8cc4-d2b71e3056b4 +version: 2 +date: '2019-04-01' +description: This search looks for process names that consist only of a single letter. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, + Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), + endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 + AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name + | `single_letter_process_on_endpoint_filter`' +known_false_positives: Single-letter executables are not always malicious. Investigate + this activity with your normal incident-response process. +tags: + analytics_story: + - DHS Report TA18-074A + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 2 + nist: + - ID.AM + - PR.DS + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/smb_traffic_spike.yml b/detections/smb_traffic_spike.yml index c020114844..013f2661fc 100644 --- a/detections/smb_traffic_spike.yml +++ b/detections/smb_traffic_spike.yml @@ -1,119 +1,37 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-20' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream +name: SMB Traffic Spike +id: 7f5fb3e1-4209-4914-90db-0ec21b936378 +version: 2 +date: '2020-01-22' description: This search looks for spikes in the number of Server Message Block (SMB) traffic connections. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src - rule_description: There was a spike in SMB traffic from $src$. - rule_title: SMB Traffic Spike from $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -7d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where - All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb - by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | - eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time - >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 - | table src count | `smb_traffic_spike_filter` ' - suppress: - suppress_fields: src - suppress_period: 28800s -eli5: Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, - is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, - which leveraged a vulnerability in the SMB protocol to propagate to other systems. - Attackers have also used SMB for lateral movement with a target environment and - to test credentials against target systems. While SMB is highly prevalent in Windows - environments, a spike in SMB traffic may still be indicative of this type of malicious - activity. This search looks for a traffic spike in SMB traffic from a particular - system. If such a spike is detected, you may want to investigate the source and - analyze the cause of the abnormal traffic. -entities: - - src how_to_implement: This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -id: 7f5fb3e1-4209-4914-90db-0ec21b936378 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic + where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb + by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats + max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, + "-70m@m"), count, null))) as count avg(eval(if(_time upperBound + AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` ' known_false_positives: A file server may experience high-demand loads that could cause this analytic to trigger. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Ransomware + - DHS Report TA18-074A + mitre_attack_id: + - T1043 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - - Execution - - Command and Control - - Commonly Used Port - mitre_technique_id: - - T1110 - - T1135 - - T1210 + - Actions on Objectives + cis20: + - CIS 8 nist: - - DE.CM -modification_date: '2020-01-22' -name: SMB Traffic Spike -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/smb_traffic_spike___mltk.yml b/detections/smb_traffic_spike___mltk.yml new file mode 100644 index 0000000000..8cc00847f4 --- /dev/null +++ b/detections/smb_traffic_spike___mltk.yml @@ -0,0 +1,58 @@ +name: SMB Traffic Spike - MLTK +id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 +version: 2 +date: '2020-01-22' +description: This search uses the Machine Learning Toolkit (MLTK) to identify spikes + in the number of Server Message Block (SMB) connections. +how_to_implement: 'To successfully implement this search, you will need to ensure + that DNS data is populating the Network_Resolution data model. In addition, the + Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your + search heads, along with any required dependencies. Finally, the support search + "Baseline of SMB Traffic - MLTK" must be executed before this detection search, + because it builds a machine-learning (ML) model over the historical data used by + this search. It is important that this search is run in the same app context as + the associated support search, so that the model created by the support search is + available for use. You should periodically re-run the support search to rebuild + the model with the latest data available in your environment.\ + + This search produces a field (Number of events,count) that are not yet supported + by ES Incident Review and therefore cannot be viewed when a notable event is raised. + This field contributes additional context to the notable. To see the additional + metadata, add the following field, if not already present, to Incident Review - + Event Attributes (Configure > Incident Management > Incident Review Settings > Add + New Entry): \ + + 1. **Label:** Number of events, **Field:** count\ + + Detailed documentation on how to create a new field within Incident Review is found + here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) + as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where + All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by + _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, + "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 + | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | + table _time src dest port count | `smb_traffic_spike___mltk_filter` ' +known_false_positives: If you are seeing more results than desired, you may consider + reducing the value of the threshold in the search. You should also periodically + re-run the support search to re-build the ML model on the latest data. Please update + the `smb_traffic_spike_mltk_filter` macro to filter out false positive results +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Ransomware + - DHS Report TA18-074A + mitre_attack_id: + - T1043 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: network + asset_type: Endpoint diff --git a/detections/smb_traffic_spike_mltk.yml b/detections/smb_traffic_spike_mltk.yml deleted file mode 100644 index b5b44c2251..0000000000 --- a/detections/smb_traffic_spike_mltk.yml +++ /dev/null @@ -1,147 +0,0 @@ -asset_type: Endpoint -baselines: - - id: df98763b-0b08-4281-8ef9-08db7ac572a9 - name: Baseline of SMB Traffic - MLTK - type: splunk -confidence: medium -creation_date: '2019-05-08' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Bro - - Splunk Stream -description: This search uses the Machine Learning Toolkit (MLTK) to identify spikes - in the number of Server Message Block (SMB) connections. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src - rule_description: There was a spike in SMB traffic from $src$ - rule_title: SMB Traffic Spike from $src$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 50 - macros: - - smb_traffic_spike_mltk_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest - values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where - All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb - by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | - eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` - | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier - | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike_mltk_filter` ' - suppress: - suppress_fields: src - suppress_period: 28800s -eli5: Attackers often leverage Server Message Block (SMB) traffic, a protocol used - for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry - ransomware, which leveraged a vulnerability in the SMB protocol to propagate to - other systems. Attackers have also used SMB for lateral movement with a target environment - and to test credentials against target systems. While SMB is highly prevalent in - Windows environments, a spike in SMB traffic may still be indicative of this type - of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) - to identify spikes in SMB traffic that are unusual for a given hour of day/day of - week combination. If such a spike is detected, you may want to investigate the source - and analyze the cause of the abnormal traffic. The determination of what is considered - an outlier may be adjusted via the threshold parameter in the search. More information - on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -entities: - - src -how_to_implement: 'To successfully implement this search, you will need to ensure - that DNS data is populating the Network_Resolution data model. In addition, the - Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your - search heads, along with any required dependencies. Finally, the support search - "Baseline of SMB Traffic - MLTK" must be executed before this detection search, - because it builds a machine-learning (ML) model over the historical data used by - this search. It is important that this search is run in the same app context as - the associated support search, so that the model created by the support search is - available for use. You should periodically re-run the support search to rebuild - the model with the latest data available in your environment.\ - - This search produces a field (Number of events,count) that are not yet supported - by ES Incident Review and therefore cannot be viewed when a notable event is raised. - This field contributes additional context to the notable. To see the additional - metadata, add the following field, if not already present, to Incident Review - - Event Attributes (Configure > Incident Management > Incident Review Settings > Add - New Entry): \ - - 1. **Label:** Number of events, **Field:** count\ - - Detailed documentation on how to create a new field within Incident Review is found - here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 -investigations: - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: If you are seeing more results than desired, you may consider - reducing the value of the threshold in the search. You should also periodically - re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Lateral Movement - - Execution - - Command and Control - - Commonly Used Port - mitre_technique_id: - - T1110 - - T1135 - - T1210 - nist: - - DE.CM -modification_date: '2020-01-22' -name: SMB Traffic Spike - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/sncd_processes.yml b/detections/sncd_processes.yml deleted file mode 100644 index 9511f0174b..0000000000 --- a/detections/sncd_processes.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-11-04' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for fast execution of processes used for system network - configuration discovery on the endpoint. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, user - rule_description: Fast execution of processes $related to network system configuration - discovery seen on $dest$. - rule_title: Fast execution of processes $process_name$ related to network - discovery seen on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - values(Processes.parent_process) as parent_process min(_time) as firstTime - max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest - Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` - | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime - dest user process_name process parent_process eventcount' - suppress: - suppress_fields: dest,user - suppress_period: 86400s -eli5: Attackers have a range of built-in Windows tools they leverage to ascertain - the topography of a network from the point of view of a compromised machine. It - is uncommon to see these commands execute quickly within short periods of time. - This search returns the number of times, as well as the first time and last times, - that every process has run for each endpoint. It then executes the macro `system_network_configuration_discovery_tools`, - which looks for processes that are typically used for network configuration discovery. - Once you have a list of suspicious process launches for each destination, you can - leverage the transaction command to see what processes are fired within a five-minute - span on an endpoint and detect only those events where the count of these processes - is greater than five. -entities: - - dest -how_to_implement: You must be ingesting data that records registry activity from your - hosts to populate the Endpoint data model in the processes node. This is typically - populated via endpoint detection-and-response products, such as Carbon Black, or - endpoint data sources, such as Sysmon. The data used for this search is usually - generated via logs that report reads and writes to the registry or that are populated - via Windows event logs, after enabling process tracking in your Windows audit settings. -id: a51bfe1a-94f0-48cc-b1e4-16ae10145893 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is uncommon for normal users to execute a series of commands - used for network discovery. System administrators often use scripts to execute these - commands. These can generate false positives. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 2 - kill_chain_phases: - - Installation - - Command and Control - - Actions on Objectives - mitre_attack: - - Execution - nist: - - ID.AM - - PR.DS -modification_date: '2018-11-20' -name: Detect processes used for System Network Configuration Discovery -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/spectre_and_meltdown_vulnerable_systems.yml b/detections/spectre_and_meltdown_vulnerable_systems.yml new file mode 100644 index 0000000000..07973b75f7 --- /dev/null +++ b/detections/spectre_and_meltdown_vulnerable_systems.yml @@ -0,0 +1,30 @@ +name: Spectre and Meltdown Vulnerable Systems +id: 354be8e0-32cd-4da0-8c47-796de13b60ea +version: 1 +date: '2017-01-07' +description: The search is used to detect systems that are still vulnerable to the + Spectre and Meltdown vulnerabilities. +how_to_implement: The search requires that you are ingesting your vulnerability-scanner + data and that it reports the CVE of the vulnerability identified. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) + as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" + OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" + by Vulnerabilities.dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `spectre_and_meltdown_vulnerable_systems_filter`' +known_false_positives: It is possible that your vulnerability scanner is not detecting + that the patches have been applied. +tags: + analytics_story: + - Spectre And Meltdown Vulnerabilities + cis20: + - CIS 4 + nist: + - ID.RA + - RS.MI + - PR.IP + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/spectre_meltdown_vulnerabilities.yml b/detections/spectre_meltdown_vulnerabilities.yml deleted file mode 100644 index 5afc6fd45e..0000000000 --- a/detections/spectre_meltdown_vulnerabilities.yml +++ /dev/null @@ -1,91 +0,0 @@ -asset_type: Endpoint -baselines: - - id: fc0edc95-ff2b-48b0-9f6f-63da3789fd61 - name: Systems Ready for Spectre-Meltdown Windows Patch - type: splunk -confidence: high -creation_date: '2018-01-07' -data_metadata: - data_models: - - Vulnerabilities - data_source: - - Vulnerability Scanner - providing_technologies: - - Nessus - - Qualys -description: The search is used to detect systems that are still vulnerable to the - Spectre and Meltdown vulnerabilities. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: $dest is vulnerable to the Spectre or Meltdown CPU vulnerabilities. - rule_title: $dest is vulnerable to the Spectre or Meltdown CPU vulnerabilities - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 100 - schedule: - cron_schedule: 0 6 * * * - earliest_time: -25h@h - latest_time: -1h@h - search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime - from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" - OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" - by Vulnerabilities.dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search looks for the three CVEs associated with the Spectre and Meltdown - vulnerabilities. -entities: - - dest -how_to_implement: The search requires that you are ingesting your vulnerability-scanner - data and that it reports the CVE of the vulnerability identified. -id: 354be8e0-32cd-4da0-8c47-796de13b60ea -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is possible that your vulnerability scanner is not detecting - that the patches have been applied. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 4 - nist: - - ID.RA - - RS.MI - - PR.IP - - DE.CM -modification_date: '2017-01-07' -name: Spectre and Meltdown Vulnerable Systems -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/spike_aws_api_call_per_account.yml b/detections/spike_aws_api_call_per_account.yml deleted file mode 100644 index 3643f7b7d2..0000000000 --- a/detections/spike_aws_api_call_per_account.yml +++ /dev/null @@ -1,155 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edc96-ff2b-48b0-9f6f-63da3783fd63 - name: Baseline of API Calls per User ARN - type: splunk -confidence: medium -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search will detect users creating spikes of API activity in your - AWS environment. It will also update the cache file that factors in the latest - data. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A spike in the number of AWS API calls by $user$ was detected. - rule_title: Spike in AWS API activity detected by $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail - eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count - as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount - | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, - 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval - avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, - stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) - | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup - api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold - = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn - as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn - | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) - as uniqueApisCalled by user - suppress: - suppress_fields: user - suppress_period: 14400s -eli5: 'This search and its corresponding subsearch run through a series of steps, - as per the following: \ - - 1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ - - 1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into - a more friendly format.\ - - 1. Counts the number of API calls per ARN.\ - - 1. Loads the cache file that contains the number of data points, the count from - the latest hour, the API call average, and the standard deviation for each ARN.\ - - 1. Drops the count from the latest hour, since it is not necessary, and merges the - rest of the data with the results of the stats command. \ - - 1. Renames `apiCalls` as `latestCount`.\ - - 1. Calculates the new average value for each ARN with the latest count, weighting - the past much more heavily than the current hour. It does the same for the standard - deviation--weighting the past more heavily than the current.\ - - 1. Updates the cache file with the latest results.\ - - 1. Sets the minimum threshold for the number of data points and sets the number - of standard deviations away from the mean it must be to be considered a spike.\ - - 1. Makes a determination regarding whether or not the current count is a spike by - checking to see if the minimum data-point threshold has been met and the count is - a sufficient number of standard deviations away from the average.\ - - 1. Filters out anything that it determines is not a spike and returns the list of - ARNs to the main search. The main search subsequently gets the names of all the - API calls, the number of unique API calls, and the total number of API calls for - each of these ARNs. Finally, it looks up the average and standard deviation and - returns both the average and the number of standard deviations the spike is from - the average.' -entities: - - user -how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the minimum number of data - points required to have a statistically significant amount of data to determine. - The `deviationThreshold` variable is the number of standard deviations away from - the mean that the value must be to be considered a spike.\ - - This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) - that are not yet supported by ES Incident Review and therefore cannot be viewed - when a notable event is raised. These fields contribute additional context to the - notable. To see the additional metadata, add the following fields, if not already - present, to Incident Review - Event Attributes (Configure > Incident Management - > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** - eventName\ - - 1. \ - - 1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ - - 1. \ - - 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: ada0f478-84a8-4641-a3f1-d32362d4bd55 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 - name: Investigate AWS User Activities by user field - type: splunk -known_false_positives: '' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Execution - nist: - - DE.DP - - DE.CM - - PR.AC -modification_date: '2018-04-09' -name: Detect Spike in AWS API Activity -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/spike_aws_security_group_activity.yml b/detections/spike_aws_security_group_activity.yml deleted file mode 100644 index fa38773664..0000000000 --- a/detections/spike_aws_security_group_activity.yml +++ /dev/null @@ -1,142 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edd96-ff2b-48b0-9f1f-63da3783fd63 - name: Baseline of Security Group Activity by ARN - type: splunk -confidence: medium -creation_date: '2018-04-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search will detect users creating spikes in API activity related - to security groups in your AWS environment. It will also update the cache file - that factors in the latest data. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A spike in the number of AWS API calls related to security - groups by $user$ was detected. - rule_title: Spike in AWS Security Group activity detected by $user$ - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail `security_group_api_calls` [search sourcetype=aws:cloudtrail - `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count - as apiCalls by arn | inputlookup security_group_activity_baseline append=t - | fields - latestCount | stats values(*) as * by arn | rename apiCalls as - latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 - | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) - | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, - stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) - | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup - security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold - = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn - as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn - | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) - as uniqueApisCalled by user - suppress: - suppress_fields: user - suppress_period: 14400s -eli5: 'This search and its corresponding subsearch run through the following series - of steps: \ - - 1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls - specifically for security groups.\ - - 1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into - a more friendly format.\ - - 1. Counts the number of API calls per ARN.\ - - 1. Loads the cache file that contains the number of data points, the count from - the latest hour, the API call average, and the standard deviation for each ARN.\ - - 1. Drops the count from the latest hour, since it is not necessary, and merges the - rest of the data with the results of the stats command. \ - - 1. Renames `apiCalls` as `latestCount`.\ - - 1. Calculates the new average value for each ARN with the latest count, weighting - the past much more heavily than the current hour. It does the same for the standard - deviation--weighting the past more heavily than the current.\ - - 1. Updates the cache file with the latest results.\ - - 1. Sets the minimum threshold for the number of data points and sets the number - of standard deviations away from the mean it must be to be considered a spike.\ - - 1. Makes a determination regarding whether or not the current count is a spike by - checking to see if the minimum data-point threshold has been met and the count is - a sufficient number of standard deviations away from the average.\ - - 1. Filters out anything that it determines is not a spike and returns the list of - ARNs to the main search. The main search subsequently gets the names of all the - API calls, the number of unique API calls, and the total number of API calls for - each of these ARNs. Finally, it looks up the average and standard deviation and - returns both the average and the number of standard deviations the spike is from - the average.' -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the minimum number of data - points required to have a statistically significant amount of data to determine. - The `deviationThreshold` variable is the number of standard deviations away from - the mean that the value must be to be considered a spike.This search works best - when you run the "Baseline of Security Group Activity by ARN" support search once - to create a history of previously seen Security Group Activity. To add or remove - API event names for security groups, edit the macro `security_group_api_calls`. -id: ada0f478-84a8-4641-a3f1-e32372d4bd53 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 - name: Investigate AWS User Activities by user field - type: splunk -known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, - the false positive rate may vary. Please modify this according the your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 16 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Execution - nist: - - DE.DP - - DE.CM - - PR.AC -modification_date: '2018-04-18' -name: Detect Spike in Security Group Activity -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/spike_in_file_writes.yml b/detections/spike_in_file_writes.yml new file mode 100644 index 0000000000..b35941f8f0 --- /dev/null +++ b/detections/spike_in_file_writes.yml @@ -0,0 +1,36 @@ +name: Spike in File Writes +id: fdb0f805-74e4-4539-8c00-618927333aae +version: 3 +date: '2020-03-16' +description: The search looks for a sharp increase in the number of files written + to a particular host +how_to_implement: In order to implement this search, you must populate the Endpoint + file-system data model node. This is typically populated via endpoint detection + and response products, such as Carbon Black or endpoint data sources such as Sysmon. + The data used for this search is typically generated via logs that report reads + and writes to the file system. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem + where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` + | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time + >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` ' +known_false_positives: It is important to understand that if you happen to install + any new applications on your hosts or are copying a large number of files, you can + expect to see a large increase of file modifications. +tags: + analytics_story: + - SamSam Ransomware + - Ransomware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/spike_s3_deletion.yml b/detections/spike_s3_deletion.yml deleted file mode 100644 index 79327bbbca..0000000000 --- a/detections/spike_s3_deletion.yml +++ /dev/null @@ -1,152 +0,0 @@ -asset_type: S3 Bucket -baselines: - - id: fc0edd96-ff2b-48b0-9f1f-63eq3783fd63 - name: Baseline of S3 Bucket deletion activity by ARN - type: splunk -confidence: medium -creation_date: '2018-07-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search detects users creating spikes in API activity related to - deletion of S3 buckets in your AWS environment. It will also update the cache file - that factors in the latest data. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: A spike in the number of S3 buckets deleted by $user$ was - detected. - rule_title: Spike detected in S3 bucket deletion activity by $user$. - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail - eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count - as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount - | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, - 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval - avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, - stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) - | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup - s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = - 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn - as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn - | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) - as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled - by user - suppress: - suppress_fields: user - suppress_period: 14400s -eli5: 'This search and its corresponding subsearch run through the following series - of steps: \ - - 1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls - specifically for deletion of S3 buckets.\ - - 1. Kick off a subsearch that retrieves the same data and pulls out and converts - the ARN into a more friendly format.\ - - 1. Count the number of API calls per ARN.\ - - 1. Load the cache file that contains the number of data points, the count from the - latest hour, the API call average, and the standard deviation for each ARN.\ - - 1. Drop the count from the latest hour, since it is unnecessary, and merge the rest - of the data with the results of the `stats` command. \ - - 1. Rename `apiCalls` as `latestCount`.\ - - 1. Calculate the new average value for each ARN with the latest count, weighting - the past more heavily than the current hour. It does the same for the standard deviation—weighting - the past more heavily than the current.\ - - 1. Update the cache file with the latest results.\ - - 1. Set the minimum threshold for the number of data points and the number of standard - deviations away from the mean it must be to be considered a spike.\ - - 1. Make a determination regarding whether or not the current count is a spike by - checking to see if the minimum data-point threshold has been met and if the count - is a sufficient number of standard deviations away from the average.\ - - 1. Filter out anything that it determines is not a spike and returns the list of - ARNs to the main search. The main search subsequently gets the names of the deleted - S3 buckets, the number of unique API calls, and the total number of API calls for - each of these user ARNs.' -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the minimum number of data - points required to have a statistically significant amount of data to determine. - The `deviationThreshold` variable is the number of standard deviations away from - the mean that the value must be to be considered a spike. This search works best - when you run the "Baseline of S3 Bucket deletion activity by ARN" support search - once to create a baseline of previously seen S3 bucket-deletion activity. -id: ad12w478-84a8-4641-a3w1-e32372q4bd53 -investigations: - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242 - name: AWS S3 Bucket details via bucketName - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 - name: Investigate AWS activities via region name - type: splunk -known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, - the false positive rate may vary. Please modify this according the your environment. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Execution - nist: - - DE.DP - - DE.CM - - PR.AC -modification_date: '2018-11-27' -name: Detect Spike in S3 Bucket deletion -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/spike_vpc_blocked_traffic.yml b/detections/spike_vpc_blocked_traffic.yml deleted file mode 100644 index a53de4f848..0000000000 --- a/detections/spike_vpc_blocked_traffic.yml +++ /dev/null @@ -1,183 +0,0 @@ -asset_type: AWS Instance -baselines: - - id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63 - name: Baseline of blocked outbound traffic from AWS - type: splunk -confidence: medium -creation_date: '2018-04-26' -data_metadata: - data_source: - - AWS VPC Flow Logs - data_sourcetypes: - - aws:cloudwatchlogs:vpcflow - providing_technologies: - - AWS -description: This search will detect spike in blocked outbound network connections - originating from within your AWS environment. It will also update the cache file - that factors in the latest data. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip - rule_description: A spike in the blocked outbound connection is detected from - source $src_ip$. - rule_title: Spike in blocked outbound network connections from $src_ip$ detected. - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 - OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND - dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search sourcetype=aws:cloudwatchlogs:vpcflow - action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) - ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | - stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections - append=t | fields - latestCount | stats values(*) as * by src_ip | rename - numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections - + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, - 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) - | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), - stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), - numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table - src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections - | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold - = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) - AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] - | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) - as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections - by src_ip - suppress: - suppress_fields: src_ip - suppress_period: 14400s -eli5: 'This search retrieves all the VPC Flow log entries that have recorded a blocked - outbound network connection originating from your AWS environment. Then it kicks - off a subsearch, which looks at the same data and performs the following series - of steps: \ - - 1. Counts the number of blocked outbound connections by each source IP\ - - 1. Loads the cache file that contains the number of data points, the count from - the latest hour, the average blocked connections, and the standard deviation for - each source IP.\ - - 1. Drops the count from the latest hour, since it is not necessary, and merges the - rest of the data with the results of the stats command. \ - - 1. Renames `numberOfBlockedConnections` as `latestCount`.\ - - 1. Calculates the new average value for each source IP with the latest count, weighting - the past much more heavily than the current hour. It does the same for the standard - deviation, weighting the past more heavily than the current.\ - - 1. Updates the cache file with the latest results.\ - - 1. Sets the minimum threshold for the number of data points and sets the number - of standard deviations away from the mean it must be to be considered a spike.\ - - 1. Makes a determination regarding whether or not the current count is a spike by - checking to see if the minimum data-point threshold has been met and the count is - a sufficient number of standard deviations away from the average.\ - - 1. Filters out anything that it determines is not a spike and returns the list of - source IPs to the main search. The main search subsequently gets the list of all - destination IPs for which the traffic was blocked, the network interface ID, the - number of unique destination IP, and the total number of blocked connections for - each of these source IP addresses. Finally, it looks up the average and standard - deviation and returns both the average and the number of standard deviations the - spike is from the average.' -entities: - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow - logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit - your environment. The `dataPointThreshold` variable is the number of data points - required to meet the definition of "spike." The `deviationThreshold` variable is - the number of standard deviations away from the mean that the value must be to be - considered a spike. This search works best when you run the "Baseline of Blocked - Outbound Connection" support search once to create a history of previously seen - blocked outbound connections. -id: ada0f278-84a8-46w1-a3f1-w32372d4bd53 -investigations: - - id: 910e6512-edc9-4f93-ba24-5b786f47a672 - name: Get Process Responsible For The DNS Traffic - type: splunk - - id: 9925d08f-561e-4faa-8912-e3888a842341 - name: Get Process Information For Port Activity - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 - name: AWS Investigate User Activities By ARN - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241 - name: AWS Network Interface details via resourceId - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242 - name: AWS Network ACL Details from ID - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 446ec87a-85c6-40d4-b060-bea4498281d6 - name: Get All AWS Activity From IP Address - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 - name: Get DNS traffic ratio - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` - and `deviationThreshold`. Additionally, false positives may result when AWS administrators - roll out policies enforcing network blocks, causing sudden increases in the number - of blocked outbound connections. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 11 - kill_chain_phases: - - Actions on Objectives - - Command and Control - mitre_attack: - - Exfiltration - - Command and Control - nist: - - DE.AE - - DE.CM - - PR.AC -modification_date: '2018-05-07' -name: Detect Spike in blocked Outbound Traffic from your AWS -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/splunk_enterprise_information_disclosure.yml b/detections/splunk_enterprise_information_disclosure.yml new file mode 100644 index 0000000000..f7cc9124d7 --- /dev/null +++ b/detections/splunk_enterprise_information_disclosure.yml @@ -0,0 +1,37 @@ +name: Splunk Enterprise Information Disclosure +id: f6a26b7b-7e80-4963-a9a8-d836e7534ebd +version: 1 +date: '2018-06-14' +description: This search allows you to look for evidence of exploitation for CVE-2018-11409, + a Splunk Enterprise Information Disclosure Bug. +how_to_implement: The REST endpoint that exposes system information is also necessary + for the proper operation of Splunk clustering and instrumentation. Whitelisting + your Splunk systems will reduce false positives. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 + uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server + as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) + as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` +known_false_positives: Retrieving server information may be a legitimate API request. + Verify that the attempt is a valid request for information. +tags: + analytics_story: + - Splunk Enterprise Vulnerability CVE-2018-11409 + kill_chain_phases: + - Delivery + cis20: + - CIS 3 + - CIS 4 + - CIS 18 + nist: + - ID.RA + - RS.MI + - PR.PT + - PR.AC + - PR.IP + - DE.CM + security_domain: network + asset_type: Splunk Server diff --git a/detections/splunk_information_disclosure.yml b/detections/splunk_information_disclosure.yml deleted file mode 100644 index c6e4ea2d3b..0000000000 --- a/detections/splunk_information_disclosure.yml +++ /dev/null @@ -1,96 +0,0 @@ -asset_type: Splunk Server -confidence: medium -creation_date: '2018-06-14' -data_metadata: - data_source: - - Splunk - data_sourcetypes: - - splunk_web_access - providing_technologies: - - Splunk Enterprise -description: This search allows you to look for evidence of exploitation for CVE-2018-11409, - a Splunk Enterprise Information Disclosure Bug. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, src_ip - rule_description: The Splunk Server $dest$ had a possible Splunk information-disclosure - possibility from $src_ip$ - rule_title: Possible Splunk Information Disclosure Exploitation Attempt from - $src_ip$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 - uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, - splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) - as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) - as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - suppress: - suppress_fields: dest, src_ip - suppress_period: 14400s -eli5: This search searches Splunk's internal logs for evidence of CVE-2018-11409 exploitation - attempts. -entities: - - dest -how_to_implement: The REST endpoint that exposes system information is also necessary - for the proper operation of Splunk clustering and instrumentation. Whitelisting - your Splunk systems will reduce false positives. -id: f6a26b7b-7e80-4963-a9a8-d836e7534ebd -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: 2f5b960b-71df-49c0-affc-74992ce60e45 - name: Investigate Web Activity From src_ip - type: splunk - - id: 9df9ca9c-a02b-4f48-9eba-0bac55179050 - name: Investigate Network Traffic From src_ip - type: splunk -known_false_positives: Retrieving server information may be a legitimate API request. - Verify that the attempt is a valid request for information. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 4 - - CIS 18 - kill_chain_phases: - - Delivery - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - nist: - - ID.RA - - RS.MI - - PR.PT - - PR.AC - - PR.IP - - DE.CM -modification_date: '2018-06-14' -name: Splunk Enterprise Information Disclosure -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/sql_injection_with_long_urls.yml b/detections/sql_injection_with_long_urls.yml index d4dd4349f9..1a26a9de54 100644 --- a/detections/sql_injection_with_long_urls.yml +++ b/detections/sql_injection_with_long_urls.yml @@ -1,107 +1,49 @@ -asset_type: Database Server -confidence: medium -creation_date: '2016-09-13' -data_metadata: - data_models: - - Web - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro +name: SQL Injection with Long URLs +id: e0aad4cf-0790-423b-8328-7564d0d938f9 +version: 1 +date: '2017-09-19' description: This search looks for long URLs that have several SQL commands visible within them. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, src, url - rule_description: Using the length of url or user agent to identify SQL injection - rule_title: SQL Injection with Long URLs - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 30 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server - AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src - Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` - | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, - "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) - + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) - + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + - mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) - + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, - "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) - + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) - + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) - + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, - "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - - 24 | where num_sql_cmds > 3' - suppress: - suppress_fields: dest,src,url - suppress_period: 14400s -eli5: This search looks only at your web servers and returns the source, the web server, - the URL and its length, and the user agent associated with HTTP GET requests for - extremely long URLs or user agent lengths with more than three common SQL commands - found within the URL. -entities: - - dest how_to_implement: To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. -id: e0aad4cf-0790-423b-8328-7564d0d938f9 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count from datamodel=Web where + Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length + > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` + | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, + "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, + "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, + "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, + "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`' known_false_positives: It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 4 - - CIS 13 - - CIS 18 +tags: + analytics_story: + - SQL Injection + mitre_attack_id: + - T1043 kill_chain_phases: - - Delivery - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - - Execution - - Commonly Used Port + - Delivery + cis20: + - CIS 4 + - CIS 13 + - CIS 18 nist: - - PR.DS - - ID.RA - - PR.PT - - PR.IP - - DE.CM -modification_date: '2017-09-19' -name: SQL Injection with Long URLs -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' + - PR.DS + - ID.RA + - PR.PT + - PR.IP + - DE.CM + security_domain: network + asset_type: Database Server diff --git a/detections/stop_security_services.yml b/detections/stop_security_services.yml deleted file mode 100644 index 2fa53a4352..0000000000 --- a/detections/stop_security_services.yml +++ /dev/null @@ -1,117 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-04-09' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for attempts to stop security-related services on the - endpoint. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, user - rule_description: Attempt to stop a security-related service on $dest$ - rule_title: Attempt to Stop Security Service On $dest$ - risk: - risk_object: src - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* - stop *" by Processes.process_name Processes.parent_process_name Processes.dest - Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - |lookup security_services_lookup service as process OUTPUTNEW category, description - | search category=security' - suppress: - suppress_fields: dest, user - suppress_period: 86400s -eli5: This search looks for the processes **net.exe** and **sc.exe** with a parameter - of `"stop"`. It then searches a list of security-related services included in a - lookup file for matches on the command line. Results are subsequently returned in - table format. The included lookup file can be modified to update the services to - monitor. -entities: - - src -how_to_implement: You must be ingesting data that records the file-system activity - from your hosts to populate the Endpoint file-system data-model node. If you are - using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which - you want to collect data. The search is shipped with a lookup file, `security_services.csv`, - that can be edited to update the list of services to monitor. This lookup file can - be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, - or via the Splunk console. You should add the names of services an attacker might - use on the command line and surround with asterisks (*****), so that they work properly - when searching the command line. The file should be updated with the names of any - services you would like to monitor for attempts to stop the service., -id: c8e349c6-b97c-486e-8949-bd7bcd1f3910 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None identified. Attempts to disable security-related services - should be identified and understood. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 8 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Disabling Security Tools - nist: - - PR.PT - - DE.CM - - PR.IP -modification_date: '2017-09-15' -name: Attempt To Stop Security Service -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/suspicious_changes_to_file_associations.yml b/detections/suspicious_changes_to_file_associations.yml new file mode 100644 index 0000000000..33457e5ff5 --- /dev/null +++ b/detections/suspicious_changes_to_file_associations.yml @@ -0,0 +1,43 @@ +name: Suspicious Changes to File Associations +id: 1b989a0e-0129-4446-a695-f193a5b746fc +version: 3 +date: '2020-03-16' +description: This search looks for changes to registry values that control Windows + file associations, executed by a process that is not typical for legitimate, routine + changes to this area. +how_to_implement: To successfully implement this search you need to be ingesting information + on registry changes that include the name of the process responsible for the changes + from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` + nodes. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) + as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe + AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest + | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | join [| tstats `security_content_summariesonly` values(Registry.registry_path) + as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* + by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table + process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` ' +known_false_positives: There may be other processes in your environment that users + may legitimately use to modify file associations. If this is the case and you are + finding false positives, you can modify the search to add those processes as exceptions. +tags: + analytics_story: + - Suspicious Windows Registry Activities + - Windows File Extension and Association Abuse + mitre_attack_id: + - T1042 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - DE.CM + - PR.PT + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/suspicious_email___uba_anomaly.yml b/detections/suspicious_email___uba_anomaly.yml new file mode 100644 index 0000000000..c4fb996355 --- /dev/null +++ b/detections/suspicious_email___uba_anomaly.yml @@ -0,0 +1,35 @@ +name: Suspicious Email - UBA Anomaly +id: 56e877a6-1455-4479-ad16-0550dc1e33f8 +version: 2 +date: '2019-07-21' +description: This detection looks for emails that are suspicious because of their + sender, domain rareness, or behavior differences. This is an anomaly generated by + Splunk User Behavior Analytics (UBA). +how_to_implement: You must be ingesting data from email logs and have Splunk integrated + with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." + Ensure that this model is enabled on your UBA instance. +type: ESCU +author: Bhavin Patel, Splunk +search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where + nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model + = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity + All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature + All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` + | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `suspicious_email___uba_anomaly_filter`' +known_false_positives: This detection model will alert on any sender domain that is + seen for the first time. This could be a potential false positive. The next step + is to investigate and whitelist the URL if you determine that it is a legitimate + sender. +tags: + analytics_story: + - Suspicious Emails + kill_chain_phases: + - Delivery + cis20: + - CIS 7 + nist: + - PR.IP + security_domain: threat + asset_type: Endpoint diff --git a/detections/suspicious_email_attachment_extensions.yml b/detections/suspicious_email_attachment_extensions.yml new file mode 100644 index 0000000000..0df813263e --- /dev/null +++ b/detections/suspicious_email_attachment_extensions.yml @@ -0,0 +1,41 @@ +name: Suspicious Email Attachment Extensions +id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084 +version: 2 +date: '2020-01-27' +description: This search looks for emails that have attachments with suspicious file + extensions. +how_to_implement: "You need to ingest data from emails. Specifically, the sender's\ + \ address and the file names of any attachments must be mapped to the Email data\ + \ model. \\\n **Splunk Phantom Playbook Integration**\\\nIf Splunk Phantom is also\ + \ configured in your environment, a Playbook called \"Suspicious Email Attachment\ + \ Investigate and Delete\" can be configured to run when any results are found by\ + \ this detection search. To use this integration, install the Phantom App for Splunk\ + \ `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the\ + \ \"Phantom Instance\" field in the Adaptive Response Actions when configuring this\ + \ detection search. The notable event will be sent to Phantom and the playbook will\ + \ gather further information about the file attachment and its network behaviors.\ + \ If Phantom finds malicious behavior and an analyst approves of the results, the\ + \ email will be deleted from the user's inbox." +type: ESCU +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, + All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | + `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` + | `suspicious_email_attachment_extensions_filter` ' +known_false_positives: None identified +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Suspicious Emails + kill_chain_phases: + - Delivery + cis20: + - CIS 3 + - CIS 7 + - CIS 12 + nist: + - DE.AE + - PR.IP + security_domain: network + asset_type: Endpoint diff --git a/detections/suspicious_email_attachments.yml b/detections/suspicious_email_attachments.yml deleted file mode 100644 index 0841e15c51..0000000000 --- a/detections/suspicious_email_attachments.yml +++ /dev/null @@ -1,93 +0,0 @@ -asset_type: Endpoint -channel: ESCU -confidence: high -creation_date: '2017-04-20' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: This search looks for emails that have attachments with suspicious file - extensions. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_user, file_name - rule_description: The sender $src_user$ has sent an email with a suspicious - file named $file_name$ - rule_title: Suspicious Email Attachment from $src_user$ - risk: - risk_object: src_user - risk_object_type: - - user - risk_score: 60 - macros: - - suspicious_email_attachments - - suspicious_email_attachments_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, - All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachments_filter` ' - suppress: - suppress_fields: src_user,message_id - suppress_period: 86400s -eli5: This search looks at any email messages with attachments and checks the file - names of those attachments against an included lookup file to see if it has a suspicious - file extension. -entities: - - src_user - - message_id -how_to_implement: "You need to ingest data from emails. Specifically, the sender's\ - \ address and the file names of any attachments must be mapped to the Email data\ - \ model. \\\n **Splunk Phantom Playbook Integration**\\\nIf Splunk Phantom is also\ - \ configured in your environment, a Playbook called \"Suspicious Email Attachment\ - \ Investigate and Delete\" can be configured to run when any results are found by\ - \ this detection search. To use this integration, install the Phantom App for Splunk\ - \ `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the\ - \ \"Phantom Instance\" field in the Adaptive Response Actions when configuring this\ - \ detection search. The notable event will be sent to Phantom and the playbook will\ - \ gather further information about the file attachment and its network behaviors.\ - \ If Phantom finds malicious behavior and an analyst approves of the results, the\ - \ email will be deleted from the user's inbox." -id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084 -investigations: - - id: 3096f721-8842-42ce-2fc7-742d8372b712 - name: Suspicious Email Attachment Investigate and Delete - type: phantom -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 7 - - CIS 12 - kill_chain_phases: - - Delivery - mitre_attack: - - Execution - - Defense Evasion - mitre_technique_id: - - T1193 - nist: - - DE.AE - - PR.IP -modification_date: '2020-01-27' -name: Suspicious Email Attachment Extensions -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/suspicious_file_write.yml b/detections/suspicious_file_write.yml new file mode 100644 index 0000000000..f833cc9b00 --- /dev/null +++ b/detections/suspicious_file_write.yml @@ -0,0 +1,40 @@ +name: Suspicious File Write +id: 57f76b8a-32f0-42ed-b358-d9fa3ca7bac8 +version: 3 +date: '2019-04-25' +description: The search looks for files created with names that have been linked to + malicious activity. +how_to_implement: You must be ingesting data that records the filesystem activity + from your hosts to populate the Endpoint file-system data model node. This is typically + populated via endpoint detection-and-response products, such as Carbon Black, or + via other endpoint data sources, such as Sysmon. The data used for this search is + typically generated via logs that report file system reads and writes. In addition, + this search leverages an included lookup file that contains the names of the files + to watch for, as well as a note to communicate why that file name is being monitored. + This lookup file can be edited to add or remove file the file names you want to + monitor. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count values(Filesystem.action) + as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` + | `suspicious_writes` | `suspicious_file_write_filter`' +known_false_positives: It's possible for a legitimate file to be created with the + same name as one noted in the lookup file. Filenames listed in the lookup file should + be unique enough that collisions are rare. Looking at the location of the file and + the process responsible for the activity can help determine whether or not the activity + is legitimate. +tags: + analytics_story: + - Hidden Cobra Malware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/suspicious_file_writes.yml b/detections/suspicious_file_writes.yml deleted file mode 100644 index 9c5ac9bc35..0000000000 --- a/detections/suspicious_file_writes.yml +++ /dev/null @@ -1,113 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2018-06-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon -description: The search looks for files created with names that have been linked to - malicious activity. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name - rule_description: A write to a filename associated with malicious activity - detected on $dest$. - rule_title: Suspicious File Write Detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Filesystem.action) as action - values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) - as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest - | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` - | `suspicious_writes`' - suppress: - suppress_fields: dest,file_name - suppress_period: 14400s -eli5: This search looks at files being created or modified in the Endpoint file-system - data model. The names of those files are checked against an included lookup file, - which contains the names of files associated with malware or attack activity. The - search returns any files with matching names, along with a note (also specified - in the lookup file) that gives or points to more information about the files. -entities: - - dest -how_to_implement: You must be ingesting data that records the filesystem activity - from your hosts to populate the Endpoint file-system data model node. This is typically - populated via endpoint detection-and-response products, such as Carbon Black, or - via other endpoint data sources, such as Sysmon. The data used for this search is - typically generated via logs that report file system reads and writes. In addition, - this search leverages an included lookup file that contains the names of the files - to watch for, as well as a note to communicate why that file name is being monitored. - This lookup file can be edited to add or remove file the file names you want to - monitor. -id: 57f76b8a-32f0-42ed-b358-d9fa3ca7bac8 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible for a legitimate file to be created with the - same name as one noted in the lookup file. Filenames listed in the lookup file should - be unique enough that collisions are rare. Looking at the location of the file and - the process responsible for the activity can help determine whether or not the activity - is legitimate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: [] - nist: - - PR.PT - - DE.CM -modification_date: '2019-04-25' -name: Suspicious File Write -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/suspicious_java_classes.yml b/detections/suspicious_java_classes.yml index 08d5e59734..f490f00a6e 100644 --- a/detections/suspicious_java_classes.yml +++ b/detections/suspicious_java_classes.yml @@ -1,102 +1,31 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-12-06' -data_metadata: - data_source: - - Web Traffic - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Apache +name: Suspicious Java Classes +id: if1fea6da-3c86-4c1d-b255-fc3b2781a491 +version: 1 +date: '2018-12-06' description: This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, url, http_user_agent - rule_description: The host $src$ with user agent $http_user_agent$ is sending - web traffic to $url$, which contains suspicious Java classes. These classes - may be indicative of remote code execution in Java frameworks, such as Apache - Struts. - rule_title: 'Suspicious Java Classes: Possible RCE against Struts or similar - Java framework from $src$' - risk: - risk_object: src - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="stream:http" http_method=POST http_content_length>1 | regex - form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as - src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, - values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent - by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - suppress: - suppress_fields: src, url, http_user_agent - suppress_period: 3600s -eli5: The search leverages HTTP form data from typically POST events that can be captured - with Splunk streams or similar wire data capture tools. The search looks for java - classes like `processbuilder` and `runtime` are used to create a new process and - execute commands inside java, and are synonymous with spawning a shell. There are - very exceptional reasons to ever these classes in Java via an HTTP API and hence - when seen are highly suspicious. Also, this is a common vectors leverage to exploit - Apache Struts. -entities: - - src how_to_implement: In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. -id: if1fea6da-3c86-4c1d-b255-fc3b2781a491 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: f5c39fac-205c-4e07-9004-8fd61ea3431a - name: Investigate Web POSTs From src - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89 - name: Investigate Suspicious Strings in HTTP Header - type: splunk -known_false_positives: There are no known false positives. -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -mappings: - cis20: - - CIS 7 - - CIS 12 - kill_chain_phases: - - Exploitation - mitre_attack: - - Execution - nist: - - DE.AE -modification_date: '2018-12-06' -name: Suspicious Java Classes -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez +type: ESCU references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: '1.0' +author: Jose Hernandez, Splunk +search: '`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" + | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) + as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) + as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | `suspicious_java_classes_filter`' +known_false_positives: There are no known false positives. +tags: + analytics_story: + - Apache Struts Vulnerability + kill_chain_phases: + - Exploitation + cis20: + - CIS 7 + - CIS 12 + nist: + - DE.AE + security_domain: threat + asset_type: Endpoint diff --git a/detections/suspicious_lnk_file_launching_a_process.yml b/detections/suspicious_lnk_file_launching_a_process.yml new file mode 100644 index 0000000000..bfcb42016c --- /dev/null +++ b/detections/suspicious_lnk_file_launching_a_process.yml @@ -0,0 +1,43 @@ +name: Suspicious LNK file launching a process +id: 5d814af1-1041-47b5-a9ac-d754e82e9a26 +version: 2 +date: '2020-03-16' +description: This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` + executing a process. This is common behavior used by various spear phishing tools. +how_to_implement: You must be ingesting data that records filesystem and process activity + from your hosts to populate the Endpoint data model. This is typically populated + via endpoint detection-and-response products, such as Carbon Black, or endpoint + data sources, such as Sysmon. +type: ESCU +author: Jose Hernandez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" + AND (Filesystem.file_path="C:\\Users*" OR Filesystem.file_path="*Local\\Temp*") by + _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash + Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid + | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id + Processes.process_name Processes.dest Processes.process_path Processes.process | + `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields + _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, + user, dest, file_name, file_path, process_name, process, process_path, file_hash + | `suspicious_lnk_file_launching_a_process_filter` ' +known_false_positives: This detection should yield little or no false positive results. + It is uncommon for LNK files to execute process from temporary or user directories. +tags: + analytics_story: + - Phishing Payloads + mitre_attack_id: + - T1193 + kill_chain_phases: + - Installation + - Actions on Objectives + cis20: + - CIS 7 + - CIS 8 + nist: + - ID.AM + - PR.DS + security_domain: network + asset_type: Endpoint diff --git a/detections/suspicious_reg_exe_process.yml b/detections/suspicious_reg_exe_process.yml new file mode 100644 index 0000000000..c60be0872b --- /dev/null +++ b/detections/suspicious_reg_exe_process.yml @@ -0,0 +1,45 @@ +name: Suspicious Reg exe Process +id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 +version: 3 +date: '2020-03-16' +description: This search looks for reg.exe being launched from a command prompt not + started by the user. When a user launches cmd.exe, the parent process is usually + explorer.exe. This search filters out those instances. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: +- https://car.mitre.org/wiki/CAR-2013-03-001 +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name + != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name + Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id + | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes + where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id + Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup + process_id| table process_id dest] | `suspicious_reg_exe_process_filter` ' +known_false_positives: It's possible for system administrators to write scripts that + exhibit this behavior. If this is the case, the search will need to be modified + to filter them out. +tags: + analytics_story: + - Windows Defense Evasion Tactics + - Disabling Security Tools + - DHS Report TA18-074A + mitre_attack_id: + - T1112 + - T1089 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/suspicious_reg_process.yml b/detections/suspicious_reg_process.yml deleted file mode 100644 index 3d511293b6..0000000000 --- a/detections/suspicious_reg_process.yml +++ /dev/null @@ -1,118 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-10-11' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for reg.exe being launched from a command prompt not - started by the user. When a user launches cmd.exe, the parent process is usually - explorer.exe. This search filters out those instances. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process_name - rule_description: The system $dest$ had reg.exe process run not initiated - by a user. - rule_title: Suspicious reg.exe process detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 80 - macros: - - suspicious_reg_process_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name - != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name - Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id - | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes - where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe - by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id - |dedup process_id| table process_id dest] | `suspicious_reg_process_filter`' - suppress: - suppress_fields: dest, user - suppress_period: 14400s -eli5: This search looks for the execution of reg.exe with a parent process of cmd.exe. - It then executes a subsearch looking for those cmd.exe processes with a parent that - is not explorer.exe. It then joins those two searches to make sure that the reg.exe - process is a grandchild of the non explorer.exe process. The search will return - the number of such instances and the first and last time this activity has been - seen on each endpoint and user. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. -id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It's possible for system administrators to write scripts that - exhibit this behavior. If this is the case, the search will need to be modified - to filter them out. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Modify Registry - - Disabling Security Tools - nist: - - DE.CM -modification_date: '2020-03-16' -name: Suspicious Reg.exe Process -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: - - https://car.mitre.org/wiki/CAR-2013-03-001 -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/suspicious_wevtutil_usage.yml b/detections/suspicious_wevtutil_usage.yml index 79f7644979..a918308805 100644 --- a/detections/suspicious_wevtutil_usage.yml +++ b/detections/suspicious_wevtutil_usage.yml @@ -1,120 +1,45 @@ -asset_type: '' -confidence: medium -creation_date: '2017-02-17' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Suspicious wevtutil Usage +id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f +version: 2 +date: '2019-02-28' description: The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. -detect: - splunk: - correlation_rule: - notable: - drilldown_name: View wevtutil process information on $dest$ - drilldown_search: '| from datamodel:Endpoint.Processes | search dest="$dest$" process=*wevtutil*' - nes_fields: dest, process, user - rule_description: wevtutil is the windows event log tool. This searches for - wevtutil clearing the security or system logs. - rule_title: Suspicious wevtutil Usage - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" - OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") - by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest,process - suppress_period: 28800s -eli5: This search looks for execution of wevtutil.exe with command-line arguments - that indicate that it has been used to delete the setup, application, security, - or system event logs. The search returns the number of times the behavior was observed, - the first and last time it was seen, the host exhibiting the behavior and the user - context of the process execution. -entities: - - dest how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` values(Processes.process) as process + min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes + where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" + OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") + by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` + | `suspicious_wevtutil_usage_filter`' known_false_positives: The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 6 +tags: + analytics_story: + - Windows Log Manipulation + - Ransomware + mitre_attack_id: + - T1070 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Indicator Removal on Host + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 6 nist: - - DE.DP - - PR.IP - - PR.PT - - PR.AC - - PR.AT - - DE.AE -modification_date: '2019-02-28' -name: Suspicious wevtutil Usage -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' + - DE.DP + - PR.IP + - PR.PT + - PR.AC + - PR.AT + - DE.AE + security_domain: endpoint + asset_type: '' diff --git a/detections/suspicious_write_to_recycler.yml b/detections/suspicious_write_to_recycler.yml deleted file mode 100644 index ccc9ea126a..0000000000 --- a/detections/suspicious_write_to_recycler.yml +++ /dev/null @@ -1,107 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-01-08' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Sysmon -description: This search detects writes to the recycle bin by a process other than - explorer.exe. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name, process_name - rule_description: The process $process_name$ on $dest$ wrote $file_name$ to - the Recycle Bin. - rule_title: Suspicious process $process_name$ wrote to the Recycle Bin on - $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) - as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path - = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| - search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) - as process_name values(Processes.parent_process_name) as parent_process_name - FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" - by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` - | table process_id dest]' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search uses data on file writes captured via Sysmon to watch for writes - to the Recycle Bin by processes other than explorer.exe. The search looks for event - code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks - for files created with a path that includes the string "$Recycle.Bin" by processes - other than explorer.exe, which is the process responsible for copying files to the - Recycle Bin on delete. It will report the system where the activity occurred, the - path to which the file was written, the process responsible for the write, and the - times it first and last saw this activity. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting information - on filesystem and process logs responsible for the changes from your endpoints into - the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -id: b5541828-8ffd-4070-9d95-b3da4de924cb -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Because the Recycle Bin is a hidden folder in modern versions - of Windows, it would be unusual for a process other than explorer.exe to write to - it. Incidents should be investigated as appropriate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - mitre_attack: - - Collection - - Data Staged - nist: - - DE.CM -modification_date: '2020-02-03' -name: Suspicious writes to windows Recycle Bin -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/suspicious_write_to_sysvol.yml b/detections/suspicious_write_to_sysvol.yml deleted file mode 100644 index 4f7c3cca3f..0000000000 --- a/detections/suspicious_write_to_sysvol.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: Windows -confidence: medium -creation_date: '2018-01-08' -data_metadata: - data_source: - - Endpoint Intel - data_sourcetypes: - - XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - providing_technologies: - - Sysmon -description: This search detects writes to the 'System Volume Information' folder - by something other than the System process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, file_name, process - rule_description: The process $process$ on $dest$ wrote $file_name$ to 'System - Volume Information'. - rule_title: Suspicious process $process$ wrote to 'System Volume Information' - on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) - EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats - count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: This search uses data on file writes captured via Sysmon to watch for writes - to the "System Volume Information" folder by processes other than the system process. - The search looks for event code 11 in the Sysmon events, which indicates a file-creation - event. It then looks for a file created with a path that includes "System Volume - Information" and a process ID (PID) other than 4. PID 4 is assigned to the System - process on Windows systems. Excluding these writes allows us to filter out legitimate - activity. It will report the system where the activity occurred, the path to which - the file was written, the process responsible for the write, and the times it first - and last saw this activity. -entities: - - dest -how_to_implement: You need to be ingesting logs with both the process name and command-line - from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 - of the Sysmon TA. -id: cd6297cd-2bdd-4aa1-84aa-5d2f84228fac -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: It is possible that other utilities or system processes may - legitimately write to this folder. Investigate and modify the search to include - exceptions as appropriate. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - mitre_attack: - - Collection - - Data Staged - nist: - - DE.CM -modification_date: '2018-01-08' -name: Suspicious writes to System Volume Information -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/suspicious_writes_to_system_volume_information.yml b/detections/suspicious_writes_to_system_volume_information.yml new file mode 100644 index 0000000000..9e26d06a6f --- /dev/null +++ b/detections/suspicious_writes_to_system_volume_information.yml @@ -0,0 +1,30 @@ +name: Suspicious writes to System Volume Information +id: cd6297cd-2bdd-4aa1-84aa-5d2f84228fac +version: 1 +date: '2018-01-08' +description: This search detects writes to the 'System Volume Information' folder + by something other than the System process. +how_to_implement: You need to be ingesting logs with both the process name and command-line + from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 + of the Sysmon TA. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: (`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ + Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, + Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `suspicious_writes_to_system_volume_information_filter` +known_false_positives: It is possible that other utilities or system processes may + legitimately write to this folder. Investigate and modify the search to include + exceptions as appropriate. +tags: + analytics_story: + - Collection and Staging + mitre_attack_id: + - T1074 + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/suspicious_writes_to_windows_recycle_bin.yml b/detections/suspicious_writes_to_windows_recycle_bin.yml new file mode 100644 index 0000000000..640a86e13c --- /dev/null +++ b/detections/suspicious_writes_to_windows_recycle_bin.yml @@ -0,0 +1,35 @@ +name: Suspicious writes to windows Recycle Bin +id: b5541828-8ffd-4070-9d95-b3da4de924cb +version: 3 +date: '2020-02-03' +description: This search detects writes to the recycle bin by a process other than + explorer.exe. +how_to_implement: To successfully implement this search you need to be ingesting information + on filesystem and process logs responsible for the changes from your endpoints into + the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) + as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" + by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| + search [| tstats `security_content_summariesonly` values(Processes.user) as user + values(Processes.process_name) as process_name values(Processes.parent_process_name) + as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name + != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` + | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`' +known_false_positives: Because the Recycle Bin is a hidden folder in modern versions + of Windows, it would be unusual for a process other than explorer.exe to write to + it. Incidents should be investigated as appropriate. +tags: + analytics_story: + - Collection and Staging + mitre_attack_id: + - T1074 + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/system_processes_run_from_unexpected_locations.yml b/detections/system_processes_run_from_unexpected_locations.yml index 9cf09a64e1..7e77f9ed36 100644 --- a/detections/system_processes_run_from_unexpected_locations.yml +++ b/detections/system_processes_run_from_unexpected_locations.yml @@ -1,111 +1,36 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2016-08-24' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: System Processes Run From Unexpected Locations +id: a34aae96-ccf8-4aef-952c-3ea21444444d +version: 5 +date: '2020-02-04' description: This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user, process_name, dest - rule_description: The system $dest$ has a process that normally runs out of - Windows\System32\ that is not being run from that location. - rule_title: System Processes Run From Unexpected Location on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - is_windows_system_file - lookups: - - is_windows_system_file - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" - Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest - Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file`' - suppress: - suppress_fields: dest,process_name - suppress_period: 86400s -eli5: This search returns all the processes that are not executing out of the C:\Windows\System32 - or C:\Windows\SysWOW64 directories. Next, it takes the filename and looks it up - in a table `is_windows_system_file` of files that should normally run out of the C:\Windows\System32 or C:\Windows\SysWOW64 - directory. Any matches are then returned. -entities: - - dest how_to_implement: To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. -id: a34aae96-ccf8-4aef-952c-3ea21444444d -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Masquerading - nist: - - PR.PT - - DE.CM -modification_date: '2020-02-04' -name: System Processes Run From Unexpected Locations -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '5.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" + Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest + Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name + Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Ransomware + mitre_attack_id: + - T1036 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/tor_traffic.yml b/detections/tor_traffic.yml index 48501ff2eb..ae0ccd1df0 100644 --- a/detections/tor_traffic.yml +++ b/detections/tor_traffic.yml @@ -1,101 +1,37 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2017-08-21' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Palo Alto Firewall - - Bro - - Splunk Stream +name: TOR Traffic +id: ea688274-9c06-4473-b951-e4cb7a5d7a45 +version: 1 +date: '2017-09-11' description: This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest_ip - rule_description: Network traffic accessing TOR detected from $src_ip$ - rule_title: TOR Network Traffic Allowed from $src_ip$ - risk: - risk_object: src_ip - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed - by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`' - suppress: - suppress_fields: src_ip - suppress_period: 28800s -eli5: The search leverages the Enterprise Security Network_Traffic data model to look - for network traffic that has been identified as TOR and marked as 'allowed'. -entities: - - src_ip how_to_implement: In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -id: ea688274-9c06-4473-b951-e4cb7a5d7a45 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: None at this time -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 9 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Command and Control - - Commonly Used Port - - Exfiltration - nist: - - DE.AE -modification_date: '2017-09-11' -name: TOR Traffic -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +type: ESCU references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed + by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` + | `tor_traffic_filter`' +known_false_positives: None at this time +tags: + analytics_story: + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Command and Control + mitre_attack_id: + - T1043 + kill_chain_phases: + - Command and Control + cis20: + - CIS 9 + - CIS 12 + nist: + - DE.AE + security_domain: network + asset_type: Endpoint diff --git a/detections/uba_email_anomaly.yml b/detections/uba_email_anomaly.yml deleted file mode 100644 index 387ed39649..0000000000 --- a/detections/uba_email_anomaly.yml +++ /dev/null @@ -1,84 +0,0 @@ -asset_type: Endpoint -channel: ESCU -confidence: medium -creation_date: '2019-07-21' -data_metadata: - data_models: - - UEBA - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: This detection looks for emails that are suspicious because of their - sender, domain rareness, or behavior differences. This is an anomaly generated by - Splunk User Behavior Analytics (UBA). -detect: - uba: - correlation_rule: - notable: - nes_fields: user, url - rule_description: Newly observed email domain $url$ sent an email to $user$. - rule_title: Suspicious Email Anomaly - risk: - risk_object: user - risk_object_type: - - user - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA - where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model - = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity - All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature - All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` - | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`' - suppress: - suppress_fields: user - suppress_period: 86400s - event_type: anomaly - model: SuspiciousEmailDetectionModel - model_version: 0.0.1 - threat_category: Email -eli5: This detection monitors for emails that are suspicious because of their sender, - domain rareness, or behavior differences, as determined by Splunk UBA. In this search, - we query the "UEBA" data model to look for anomalies that are raised by the "SuspiciousEmailDetectionModel" - and will output the count, description of the anomaly, signature, the type of event - in UBA, the severity, and the user who received a potentially suspicious email from - a newly seen domain. It will also output all the categories associated with that - anomaly. -entities: - - user - - url -how_to_implement: You must be ingesting data from email logs and have Splunk integrated - with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." - Ensure that this model is enabled on your UBA instance. -id: 56e877a6-1455-4479-ad16-0550dc1e33f8 -known_false_positives: This detection model will alert on any sender domain that is - seen for the first time. This could be a potential false positive. The next step - is to investigate and whitelist the URL if you determine that it is a legitimate - sender. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 7 - kill_chain_phases: - - Delivery - mitre_attack: [] - nist: - - PR.IP -modification_date: '2019-07-21' -name: Suspicious Email - UBA Anomaly -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -security_domain: threat -spec_version: 2 -type: uba -version: '2.0' diff --git a/detections/unauthorized_assets_via_mac_address.yml b/detections/unauthorized_assets_via_mac_address.yml deleted file mode 100644 index 257d0e2049..0000000000 --- a/detections/unauthorized_assets_via_mac_address.yml +++ /dev/null @@ -1,100 +0,0 @@ -asset_type: Infrastructure -baselines: - - id: dcfd6b40-42f9-469d-a433-2e53f7489ff9 - name: Count of assets by category - type: splunk -confidence: medium -creation_date: '2017-06-11' -data_metadata: - data_models: - - Network_Sessions - data_source: - - Network Communication - providing_technologies: - - Splunk Stream - - Bro -description: By populating the organization's assets within the assets_by_str.csv, - we will be able to detect unauthorized devices that are trying to connect with the - organization's network by inspecting DHCP request packets, which are issued by devices - when they attempt to obtain an IP address from the DHCP server. The MAC address - associated with the source of the DHCP request is checked against the list of known - devices, and reports on those that are not found. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src, query - rule_description: The host $src$ issued a DHCP request to connect with your - network that does not belong to the list of authorized devices - rule_title: 'Unauthorized Asset found with mac address: $src_mac$' - risk: - risk_object: src - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where - nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip - All_Sessions.src_mac | dedup All_Sessions.src_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` - | search NOT [| inputlookup asset_lookup_by_str |rename mac as src_mac | fields - + src_mac]' - suppress: - suppress_fields: src_mac,src_ip - suppress_period: 86400s -eli5: 'This search requires you to leverage the Enterprise Security Assets and Identity - framework to populate assets_by_str.csv. Once the assets_by_str.csv is populated, - we then query your DHCP logs to detect unknown systems connecting to your network. - More documentation is available at: http://docs.splunk.com/Documentation/ES/4.7.1/Admin/Verifyassetandidentitydata.' -entities: - - src -how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise - Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv - file located in SA-IdentityManagement, which will contain a list of known authorized - organizational assets including their MAC addresses. Ensure that all inventoried - systems have their MAC address populated. -id: dcfd6b40-42f9-469d-a433-2e53f7489ff4 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd33 - name: Get First Occurrence and Last Occurrence of a MAC Address - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk -known_false_positives: This search might be prone to high false positives. Please - consider this when conducting analysis or investigations. Authorized devices may - be detected as unauthorized. If this is the case, verify the MAC address of the - system responsible for the false positive and add it to the Assets and Identity - framework with the proper information. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 1 - kill_chain_phases: - - Reconnaissance - - Delivery - - Actions on Objectives - mitre_attack: - - Defense Evasion - nist: - - ID.AM - - PR.DS -modification_date: '2017-09-13' -name: Detect Unauthorized Assets by MAC address -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/unauthorized_dns_servers.yml b/detections/unauthorized_dns_servers.yml deleted file mode 100644 index d5607a928c..0000000000 --- a/detections/unauthorized_dns_servers.yml +++ /dev/null @@ -1,89 +0,0 @@ -asset_type: Endpoint -baselines: [] -confidence: medium -creation_date: '2017-07-08' -data_metadata: - data_models: - - Network_Resolution - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: This search will detect DNS requests resolved by unauthorized DNS servers. - Legitimate DNS servers should be identified in the Enterprise Security Assets and - Identity Framework. -detect: - splunk: - correlation_rule: - macros: - - unauthorized_dns_servers_filter - notable: - nes_fields: dest, src - rule_description: The table represents a list of unauthorized DNS servers - interacting with hosts in your network - rule_title: DNS requests resolved by unauthorized DNS servers - risk: - risk_object: src - risk_object_type: - - system - risk_score: 40 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where - DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src - DNS.dest | `drop_dm_object_name("DNS")` | `unauthorized_dns_servers_filter`' - suppress: - suppress_fields: dest,src - suppress_period: 28800s -eli5: Clients should be resolving their DNS requests via a trusted DNS server. This - search will identify DNS queries being sent to unauthorized DNS servers by comparing - the destination and source of the traffic with assets marked as DNS servers. -entities: - - dest - - src -how_to_implement: To successfully implement this search you will need to ensure that - DNS data is populating the Network_Resolution data model. It also requires that - your DNS servers are identified correctly in the Assets and Identity table of Enterprise - Security. -id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 - name: Get DNS Server History for a host - type: splunk -known_false_positives: Legitimate DNS activity can be detected in this search. Investigate, - verify and update the list of authorized DNS servers as appropriate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 1 - - CIS 3 - - CIS 8 - - CIS 12 - kill_chain_phases: - - Command and Control - mitre_attack: - - Exfiltration - - Defense Evasion - nist: - - ID.AM - - PR.DS - - PR.IP - - DE.AE - - DE.CM -modification_date: '2020-01-17' -name: DNS Query Requests Resolved by Unauthorized DNS Servers -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -responses: [] -security_domain: network -spec_version: 2 -type: splunk -version: '2.0' diff --git a/detections/uncommon_processes.yml b/detections/uncommon_processes.yml deleted file mode 100644 index 5b3232f774..0000000000 --- a/detections/uncommon_processes.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-12-08' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search looks for applications on the endpoint that you have marked - as uncommon. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process_name, user - rule_description: Prohibited software $process_name$ has been detected on - $dest$ - rule_title: Prohibited Software Detected On $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - uncommon_processes_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user - Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_filter`' - suppress: - suppress_fields: dest, user - suppress_period: 86400s -eli5: This search returns the number of times, as well as the first and last time, - it has seen every process run for each endpoint and user, and then displays only - those processes that you have marked as uncommon in the `uncommon_processes_default.csv` - table. -entities: - - dest -how_to_implement: You must be ingesting data that records process activity from your - hosts to populate the Endpoint data model in the Processes node. You must also be - ingesting logs with both the process name and command line from your endpoints. - The command-line arguments are mapped to the "process" field in the Endpoint data - model. This search uses a lookup file `uncommon_processes_default.csv` to track - various features of process names that are usually uncommon in most environments. - Please consider updating `uncommon_processes_local.csv` to hunt for processes that - are uncommon in your environment. -id: 29ccce64-a10c-4389-a45f-337cb29ba1f7 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: None identified -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 2 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Accessibility Features - nist: - - ID.AM - - PR.DS -modification_date: '2020-03-16' -name: Uncommon Processes On Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/uncommon_processes_on_endpoint.yml b/detections/uncommon_processes_on_endpoint.yml new file mode 100644 index 0000000000..0ced12b497 --- /dev/null +++ b/detections/uncommon_processes_on_endpoint.yml @@ -0,0 +1,37 @@ +name: Uncommon Processes On Endpoint +id: 29ccce64-a10c-4389-a45f-337cb29ba1f7 +version: 3 +date: '2020-03-16' +description: This search looks for applications on the endpoint that you have marked + as uncommon. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. This search uses a lookup file `uncommon_processes_default.csv` to track + various features of process names that are usually uncommon in most environments. + Please consider updating `uncommon_processes_local.csv` to hunt for processes that + are uncommon in your environment. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process + Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` ' +known_false_positives: None identified +tags: + analytics_story: + - Windows Privilege Escalation + - Unusual Processes + mitre_attack_id: + - T1015 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 2 + nist: + - ID.AM + - PR.DS + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/unload_sysmon_filter_driver.yml b/detections/unload_sysmon_filter_driver.yml index 9030137c47..48d0404d38 100644 --- a/detections/unload_sysmon_filter_driver.yml +++ b/detections/unload_sysmon_filter_driver.yml @@ -1,103 +1,37 @@ -asset_type: '' -confidence: medium -creation_date: '2017-10-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. - rule_title: 'Unload Sysmon Filter Driver on $dest$ ' - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - unload_sysmon_filter_driver_filter - - schedule: - cron_schedule: 30 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) - as process max(_time) as lastTime from datamodel=Endpoint.Processes where - Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by - Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process' - suppress: - suppress_fields: dest, process - suppress_period: 86400s -eli5: This search is looking to detect execution of `fltMC.exe` that specifically used for unloading the Sysmon Filter Driver -entities: - - dest +name: Unload Sysmon Filter Driver +id: c77162d3-f93c-45cc-80c8-22f665664g9f +version: 2 +date: '2020-01-13' +description: Attackers often disable security tools to avoid detection. This search + looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop + sysmon from collecting the data. how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data - model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. -id: c77162d3-f93c-45cc-80c8-22f665664g9f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: '' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Disabling Security Tools - mitre_technique_id: - - T1089 - nist: - - DE.CM -modification_date: '2020-01-13' -name: Unload Sysmon Filter Driver -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel + model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, + update this macro to filter out false positives. +type: ESCU references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '2.0' +author: Bhavin Patel, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) + as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe + AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name + Processes.process_id Processes.parent_process_name Processes.process Processes.dest + Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count + process_name process_id parent_process_name process' +known_false_positives: '' +tags: + analytics_story: + - Disabling Security Tools + mitre_attack_id: + - T1089 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/unsigned_image_loaded_by_LSASS.yml b/detections/unsigned_image_loaded_by_LSASS.yml index 4d6561618e..d614857984 100644 --- a/detections/unsigned_image_loaded_by_LSASS.yml +++ b/detections/unsigned_image_loaded_by_LSASS.yml @@ -1,89 +1,35 @@ -asset_type: Windows -confidence: medium -creation_date: '2019-12-06' -data_metadata: - data_eventtypes: - - wineventlog_sysmon - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search detects loading of unsigned images by LSASS. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Possible attempt at credential dumping was detected on $dest$. - rule_title: unsigned images loaded by LSASS on $dest$. - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - sysmon - - unsigned_image_loaded_by_LSASS_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '`sysmon` EventID=7 Image=*lsass.exe Signed=false - | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 - | rename Computer as dest - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - | `unsigned_image_loaded_by_LSASS_filter`' - suppress: - suppress_fields: dest, ImageLoaded - suppress_period: 86400s -eli5: This search detects unsigned images loaded by LSASS (Local Security Authrity Subsystem Service). - Normally, LSASS only loads signed images. Therefore, it is a malicious indicator when unsigned images are loaded by LSASS. - This can be an indicator for credential dumping using tools like Windows Credential Editor. -entities: - - dest -how_to_implement: 'This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.' +name: Unsigned Image Loaded by LSASS id: 56ef054c-76ef-45f9-af4a-a634695dcd65 -investigations: - - id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad - name: Investigate Previous Unseen User - type: splunk - - id: 097e8030-8662-4254-a735-bf0bdda696e3 - name: Investigate Failed Logins for Multiple Destinations - type: splunk - - id: ed3fff45-cba6-4990-983f-6fac72bee659 - name: Investigate Pass the Hash Attempts - type: splunk - - id: 990007ad-d798-4b29-ab2f-f0034144c937 - name: Investigate Pass the Ticket Attempts - type: splunk +version: 1 +date: '2019-12-06' +description: This search detects loading of unsigned images by LSASS. +how_to_implement: This search needs Sysmon Logs with a sysmon configuration, which + includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. + We strongly recommend that you specify your environment-specific configurations + (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +type: ESCU +references: +- https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf +author: Patrick Bareiss, Splunk +search: '`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) + as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 + | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | `unsigned_image_loaded_by_LSAS`unsigned_image_loaded_by_lsass_filter` ' known_false_positives: Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -mappings: - cis20: - - CIS 8 - - CIS 16 +tags: + analytics_story: + - Credential Dumping + mitre_attack_id: + - T1003 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Credential Access - - Credential Dumping - mitre_technique_id: - - T1003 + - Actions on Objectives + cis20: + - CIS 8 + - CIS 16 nist: - - DE.CM -modification_date: '2019-12-06' -name: Unsigned Image Loaded by LSASS -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -references: - - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf -security_domain: endpoint -spec_version: 2 -type: splunk -version: "1" + - DE.CM + security_domain: endpoint + asset_type: Windows diff --git a/detections/unsuccessful_netbackup_backups.yml b/detections/unsuccessful_netbackup_backups.yml new file mode 100644 index 0000000000..92f94a59a8 --- /dev/null +++ b/detections/unsuccessful_netbackup_backups.yml @@ -0,0 +1,27 @@ +name: Unsuccessful Netbackup backups +id: a34aae96-ccf8-4aaa-952c-3ea21444444f +version: 1 +date: '2017-09-12' +description: This search gives you the hosts where a backup was attempted and then + failed. +how_to_implement: To successfully implement this search you need to obtain data from + your backup solution, either from the backup logs on your endpoints or from a central + server responsible for performing the backups. If you do not use Netbackup, you + can modify this search for your specific backup solution. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '`netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE + | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` + | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature + | `unsuccessful_netbackup_backups_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Monitor Backup Solution + cis20: + - CIS 10 + nist: + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/unusually_long_command_line.yml b/detections/unusually_long_command_line.yml new file mode 100644 index 0000000000..434cd4e457 --- /dev/null +++ b/detections/unusually_long_command_line.yml @@ -0,0 +1,37 @@ +name: Unusually Long Command Line +id: c77162d3-f93c-45cc-80c8-22f6a4264e7f +version: 4 +date: '2020-03-16' +description: Command lines that are extremely long may be indicative of malicious + activity on your hosts. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships, from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name + Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) + as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) + as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | + `unusually_long_command_line_filter` | eval threshold = 10 | where maxlen > ((threshold*stdevperhost) + + avgperhost)' +known_false_positives: Some legitimate applications start with long command lines. +tags: + analytics_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/unusually_long_command_line___mltk.yml b/detections/unusually_long_command_line___mltk.yml new file mode 100644 index 0000000000..dea463a213 --- /dev/null +++ b/detections/unusually_long_command_line___mltk.yml @@ -0,0 +1,50 @@ +name: Unusually Long Command Line - MLTK +id: 57edaefa-a73b-45e5-bbae-f39c1473f941 +version: 1 +date: '2019-05-08' +description: Command lines that are extremely long may be indicative of malicious + activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) + to help identify command lines with lengths that are unusual for a given user. +how_to_implement: You must be ingesting endpoint data that monitors command lines + and populates the Endpoint data model in the Processes node. The command-line arguments + are mapped to the "process" field in the Endpoint data model. In addition, MLTK + version >= 4.2 must be installed on your search heads, along with any required dependencies. + Finally, the support search "Baseline of Command Line Length - MLTK" must be executed + before this detection search, as it builds an ML model over the historical data + used by this search. It is important that this search is run in the same app context + as the associated support search, so that the model created by the support search + is available for use. You should periodically re-run the support search to rebuild + the model with the latest data available in your environment. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name + Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| + `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown + | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier + | search isOutlier > 0 | table firstTime lastTime user dest process_name process + processlen count | `unusually_long_command_line___mltk_filter`' +known_false_positives: Some legitimate applications use long command lines for installs + or updates. You should review identified command lines for legitimacy. You may modify + the first part of the search to omit legitimate command lines from consideration. + If you are seeing more results than desired, you may consider changing the value + of threshold in the search to a smaller value. You should also periodically re-run + the support search to re-build the ML model on the latest data. You may get unexpected + results if the user identified in the results is not present in the data used to + build the associated model. +tags: + analytics_story: + - Suspicious Command-Line Executions + - Unusual Processes + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 8 + nist: + - PR.PT + - DE.CM + security_domain: endpoint + asset_type: '' diff --git a/detections/unusually_long_commandlines.yml b/detections/unusually_long_commandlines.yml deleted file mode 100644 index cbebca5e07..0000000000 --- a/detections/unusually_long_commandlines.yml +++ /dev/null @@ -1,112 +0,0 @@ -asset_type: '' -confidence: medium -creation_date: '2017-08-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Command lines that are extremely long may be indicative of malicious - activity on your hosts. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, user - rule_description: An unusually long command line $cmdline$ was found on $dest$ - rule_title: Unusually Long Command Line on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - macros: - - unusually_long_commandlines_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -1d@d - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest - Processes.process_name Processes.process | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats - stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) - as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, - user, process_name, process| `unusually_long_commandlines_filter`| eval threshold = 10 | where maxlen > ((threshold*stdevperhost) - + avgperhost)' - suppress: - suppress_fields: dest,user,process - suppress_period: 28800s -eli5: This search calculates the average and standard deviation for the length of - the command lines on each of your endpoints and alerts when it detects a command - line with a length over 10 times the standard deviation larger than the average - command line. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships, from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: c77162d3-f93c-45cc-80c8-22f6a4264e7f -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Some legitimate applications start with long command lines. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - PR.PT - - DE.CM -modification_date: '2020-03-16' -name: Unusually Long Command Line -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '4.0' diff --git a/detections/unusually_long_commandlines_mltk.yml b/detections/unusually_long_commandlines_mltk.yml deleted file mode 100644 index bd665287b7..0000000000 --- a/detections/unusually_long_commandlines_mltk.yml +++ /dev/null @@ -1,123 +0,0 @@ -asset_type: '' -baselines: - - id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459 - name: Baseline of Command Line Length - MLTK - type: splunk -confidence: medium -creation_date: '2019-05-08' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: Command lines that are extremely long may be indicative of malicious - activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) - to help identify command lines with lengths that are unusual for a given user. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, process, user - rule_description: An unusually long command line $cmdline$ was found on $dest$ - rule_title: Unusually Long Command Line on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 50 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count' - suppress: - suppress_fields: dest,user,process_name - suppress_period: 28800s -eli5: This search leverages the Machine Learning Toolkit (MLTK) to identify outliers - in the length of the command lines observed to be used by a specific user. The companion - search, "Baseline of Command Line Length - MLTK," creates a machine-learning (ML) - model built over the historical data used by this search. The determination of what - is considered an outlier may be adjusted via the threshold parameter in the search. - More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -entities: - - dest - - process_name - - user -how_to_implement: You must be ingesting endpoint data that monitors command lines - and populates the Endpoint data model in the Processes node. The command-line arguments - are mapped to the "process" field in the Endpoint data model. In addition, MLTK - version >= 4.2 must be installed on your search heads, along with any required dependencies. - Finally, the support search "Baseline of Command Line Length - MLTK" must be executed - before this detection search, as it builds an ML model over the historical data - used by this search. It is important that this search is run in the same app context - as the associated support search, so that the model created by the support search - is available for use. You should periodically re-run the support search to rebuild - the model with the latest data available in your environment. -id: 57edaefa-a73b-45e5-bbae-f39c1473f941 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 - name: Investigate Web Activity From Host - type: splunk -known_false_positives: Some legitimate applications use long command lines for installs - or updates. You should review identified command lines for legitimacy. You may modify - the first part of the search to omit legitimate command lines from consideration. - If you are seeing more results than desired, you may consider changing the value - of threshold in the search to a smaller value. You should also periodically re-run - the support search to re-build the ML model on the latest data. You may get unexpected - results if the user identified in the results is not present in the data used to - build the associated model. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 8 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - nist: - - PR.PT - - DE.CM -modification_date: '2019-05-08' -name: Unusually Long Command Line - MLTK -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/unusually_long_content-type_length.yml b/detections/unusually_long_content-type_length.yml deleted file mode 100644 index cc4acc246e..0000000000 --- a/detections/unusually_long_content-type_length.yml +++ /dev/null @@ -1,98 +0,0 @@ -asset_type: Web Server -confidence: high -creation_date: '2017-03-14' -data_metadata: - data_source: - - Web Server - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search looks for unusually long strings in the Content-Type http - header that the client sends the server. -detect: - splunk: - correlation_rule: - notable: - nes_fields: src_ip, dest_ip, url - rule_description: This search looks for unusually long strings in the Content-Type - http header - rule_title: Unusually Long Content-Type Length - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype=stream:http | eval cs_content_type_length = len(cs_content_type) - | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length - cs_content_type url - suppress: - suppress_fields: dest_ip - suppress_period: 28800s -eli5: This detection search uses HTTP traffic data captured with Splunk Stream. The - search is constructed to use "stream:http" sourcetype and counts of the number of - times an HTTP request is received by a destination which the length of the Content-Type - header value the client sends the server is greater than 100 characters long. We - calculate this content_type_length field and output the results. -entities: - - dest -how_to_implement: This particular search leverages data extracted from Stream:HTTP. - You must configure the http stream using the Splunk Stream App on your Splunk Stream - deployment server to extract the cs_content_type field. -id: 57a0a2bf-353f-40c1-84dc-29293f3c35b7 -investigations: - - id: f5c39fac-205c-4e07-9004-8fd61ea3431a - name: Investigate Web POSTs From src - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89 - name: Investigate Suspicious Strings in HTTP Header - type: splunk -known_false_positives: Very few legitimate Content-Type fields will have a length - greater than 100 characters. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 3 - - CIS 4 - - CIS 18 - - CIS 12 - kill_chain_phases: - - Delivery - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - nist: - - ID.RA - - RS.MI - - PR.PT - - PR.IP - - DE.AE - - PR.MA - - DE.CM -modification_date: '2017-10-13' -name: Unusually Long Content-Type Length -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/unusually_long_content_type_length.yml b/detections/unusually_long_content_type_length.yml new file mode 100644 index 0000000000..be724f2a1a --- /dev/null +++ b/detections/unusually_long_content_type_length.yml @@ -0,0 +1,37 @@ +name: Unusually Long Content-Type Length +id: 57a0a2bf-353f-40c1-84dc-29293f3c35b7 +version: 1 +date: '2017-10-13' +description: This search looks for unusually long strings in the Content-Type http + header that the client sends the server. +how_to_implement: This particular search leverages data extracted from Stream:HTTP. + You must configure the http stream using the Splunk Stream App on your Splunk Stream + deployment server to extract the cs_content_type field. +type: ESCU +references: [] +author: Bhavin Patel, Splunk +search: '`stream_http` | eval cs_content_type_length = len(cs_content_type) | where + cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length + cs_content_type url | `unusually_long_content_type_length_filter`' +known_false_positives: Very few legitimate Content-Type fields will have a length + greater than 100 characters. +tags: + analytics_story: + - Apache Struts Vulnerability + kill_chain_phases: + - Delivery + cis20: + - CIS 3 + - CIS 4 + - CIS 18 + - CIS 12 + nist: + - ID.RA + - RS.MI + - PR.PT + - PR.IP + - DE.AE + - PR.MA + - DE.CM + security_domain: network + asset_type: Web Server diff --git a/detections/usb_insertion.yml b/detections/usb_insertion.yml deleted file mode 100644 index 23886ea679..0000000000 --- a/detections/usb_insertion.yml +++ /dev/null @@ -1,106 +0,0 @@ -asset_type: Endpoint -confidence: low -creation_date: '2017-08-03' -data_metadata: - data_models: - - Change_Analysis - data_source: - - Windows Security Audit logs - providing_technologies: - - Microsoft Windows -description: The search is used to detect hosts that generate Windows Event ID 4663 - for successful attempts to write to or read from a removable storage and Event ID - 4656 for failures, which occurs when a USB drive is plugged in. In this scenario - we are querying the Change_Analysis data model to look for Windows Event ID 4656 - or 4663 where the priority of the affected host is marked as high in the ES Assets - and Identity Framework. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: Read/Write attempt to a USB was detected on this host - rule_title: Read/Write attempt to a USB detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 20 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) - AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable - Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) - (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| - `security_content_ctime(earliest)`| `security_content_ctime(latest)` ' - suppress: - suppress_fields: dest - suppress_period: 86400s -eli5: USB is a common attack vector for delivering or propagating malicious code, - or the exfiltration of data. Your corporation may have a policy of not allowing - removable media at all, or may only allow approved media to be used on specific - hosts by specific users. By logging USB activity from Windows and other endpoints - gathered using the Universal Forwarder, you can gain an understanding of what systems - might be vulnerable to attack via removable media, or what users might need additional - security training. This search is looking for event_id 4656 for failure and 4663 - for successful USB read/write attempts from Windows Security Event logs, which is - the event code generated when a files are read from and written to a removable storage - device -entities: - - dest -how_to_implement: To successfully implement this search, you must ingest Windows Security - Event logs and track event code 4663 and 4656. Ensure that the field from the event - logs is being mapped to the result_id field in the Change_Analysis data model. To - minimize the alert volume, this search leverages the Assets and Identity framework - to filter out events from those assets not marked high priority in the Enterprise - Security Assets and Identity Framework. -id: 104658f4-afdc-499f-9719-17a43f9826f5 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Legitimate USB activity will also be detected. Please verify - and investigate as appropriate. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 13 - kill_chain_phases: - - Installation - - Actions on Objectives - mitre_attack: - - Exfiltration - nist: - - PR.PT - - PR.DS -modification_date: '2017-11-27' -name: Detect USB device insertion -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/users_without_mfa.yml b/detections/users_without_mfa.yml deleted file mode 100644 index f7f12d6842..0000000000 --- a/detections/users_without_mfa.yml +++ /dev/null @@ -1,111 +0,0 @@ -asset_type: AWS Instance -confidence: medium -creation_date: '2018-05-17' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search looks for CloudTrail events where a user logged into the - AWS account, is making API calls and has not enabled Multi Factor authentication. - Multi factor authentication adds a layer of security by forcing the users to type - a unique authentication code from an approved authentication device when they access - AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged - IAM users. -detect: - splunk: - correlation_rule: - notable: - nes_fields: user - rule_description: API Activity detected from $user$ without MFA enabled. - rule_title: API Activity detected from $user$ without MFA enabled - risk: - risk_object: user - risk_object_type: - - user - risk_score: 30 - schedule: - cron_schedule: 0 8 * * * - earliest_time: -1d@d - latest_time: -10m@m - search: sourcetype=aws:cloudtrail userIdentity.sessionContext.attributes.mfaAuthenticated=false - | search NOT [| inputlookup aws_service_accounts | fields identity | rename - identity as user]| stats count min(_time) as firstTime max(_time) as lastTime - values(eventName) as eventName by userIdentity.arn userIdentity.type user - | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` - suppress: - suppress_fields: user - suppress_period: 84600s -eli5: ' In this search, we query CloudTrail logs and specifically look for events - where the multi factor authentication context of the user''s session is false which - basically means, that the user does not have MFA enabled on AWS. We then filter - out all the known AWS service accounts since service accounts typically do not have - MFA enabled. The search then creates a table of the first and last time a user without - MFA was detected, the values and count of the API calls made, the type of user identity, - ARN and the name of the user.' -entities: - - user -how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. Leverage the support search `Create a list of approved AWS service accounts`: - run it once every 30 days to create a list of service accounts and validate them.\ - - This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) - that are not yet supported by ES Incident Review and therefore cannot be viewed - when a notable event is raised. These fields contribute additional context to the - notable. To see the additional metadata, add the following fields, if not already - present, to Incident Review - Event Attributes (Configure > Incident Management - > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** - eventName\ - - 1. \ - - 1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ - - 1. \ - - 1. **Label:** AWS User Type, **Field:** userIdentity.type\ - - Detailed documentation on how to create a new field within Incident Review may be - found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`' -id: 2a9b80d3-6340-4345-w5ad-212bf5d1dac4 -investigations: - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 - name: Investigate AWS User Activities by user field - type: splunk -known_false_positives: Many service accounts configured within an AWS infrastructure - do not have multi factor authentication enabled. Please ignore the service accounts, - if triggered and instead add them to the aws_service_accounts.csv file to fine tune - the detection. It is also possible that the search detects users in your environment - using Single Sign-On systems, since the MFA is not handled by AWS. -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -mappings: - cis20: - - CIS 16 - mitre_attack: - - Execution - nist: - - DE.DP - - PR.AC -modification_date: '2018-05-17' -name: Detect API activity from users without MFA -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: [] -security_domain: network -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/usn_journal_deletion.yml b/detections/usn_journal_deletion.yml new file mode 100644 index 0000000000..82ac91f6af --- /dev/null +++ b/detections/usn_journal_deletion.yml @@ -0,0 +1,43 @@ +name: USN Journal Deletion +id: b6e0ff70-b122-4227-9368-4cf322ab43c3 +version: 2 +date: '2018-12-03' +description: The fsutil.exe application is a legitimate Windows utility used to perform + tasks related to the file allocation table (FAT) and NTFS file systems. The update + sequence number (USN) change journal provides a log of all changes made to the files + on the disk. This search looks for fsutil.exe deleting the USN journal. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You must also be + ingesting logs with both the process name and command line from your endpoints. + The command-line arguments are mapped to the "process" field in the Endpoint data + model. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count values(Processes.process) + as process values(Processes.parent_process) as parent_process min(_time) as firstTime + max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe + by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | + `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter`' +known_false_positives: None identified +tags: + analytics_story: + - Windows Log Manipulation + - Ransomware + mitre_attack_id: + - T1070 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 6 + - CIS 8 + - CIS 10 + nist: + - DE.CM + - PR.PT + - DE.AE + - DE.DP + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/web_fraud___account_harvesting.yml b/detections/web_fraud___account_harvesting.yml new file mode 100644 index 0000000000..e3a7425763 --- /dev/null +++ b/detections/web_fraud___account_harvesting.yml @@ -0,0 +1,49 @@ +name: Web Fraud - Account Harvesting +id: 31337aaa-941d-4ada-81ac-q2a17be5bf0d +version: 1 +date: '2018-10-08' +description: This search is used to identify the creation of multiple user accounts + using the same email domain name. +how_to_implement: We start with a dataset that provides visibility into the email + address used for the account creation. In this example, we are narrowing our search + down to the single web page that hosts the Magento2 e-commerce platform (via URI) + used for account creation, the single http content-type to grab only the user's + clicks, and the http field that provides the username (form_data), for performance + reasons. After we have the username and email domain, we look for numerous account + creations per email domain. Common data sources used for this detection are customized + Apache logs or Splunk Stream. +type: ESCU +references: +- https://splunkbase.splunk.com/app/2734/ +- https://splunkbase.splunk.com/app/1809/ +author: Jim Apger, Splunk +search: '`stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" + | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" + | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) + as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> + 25 | `web_fraud___account_harvesting_filter`' +known_false_positives: As is common with many fraud-related searches, we are usually + looking to attribute risk or synthesize relevant context with loosely written detections + that simply detect anamolous behavior. This search will need to be customized to + fit your environment—improving its fidelity by counting based on something + much more specific, such as a device ID that may be present in your dataset. Consideration + for whether the large number of registrations are occuring from a first-time seen + domain may also be important. Extending the search window to look further back + in time, or even calculating the average per hour/day for each email domain to look + for an anomalous spikes, will improve this search. You can also use Shannon entropy + or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness + or similarity of the email name or email domain, as the names are often machine-generated. +tags: + analytics_story: + - Web Fraud Detection + mitre_attack_id: + - T1136 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 16 + nist: + - DE.CM + - DE.DP + security_domain: threat + asset_type: Account diff --git a/detections/web_fraud___anomalous_user_clickspeed.yml b/detections/web_fraud___anomalous_user_clickspeed.yml new file mode 100644 index 0000000000..af980c23eb --- /dev/null +++ b/detections/web_fraud___anomalous_user_clickspeed.yml @@ -0,0 +1,45 @@ +name: Web Fraud - Anomalous User Clickspeed +id: 31337bbb-bc22-4752-b599-ef192df2dc7a +version: 1 +date: '2018-10-08' +description: This search is used to examine web sessions to identify those where the + clicks are occurring too quickly for a human or are occurring with a near-perfect + cadence (high periodicity or low standard deviation), resembling a script driven + session. +how_to_implement: Start with a dataset that allows you to see clickstream data for + each user click on the website. That data must have a time stamp and must contain + a reference to the session identifier being used by the website. This ties the clicks + together into clickstreams. This value is usually found in the http cookie. With + a bit of tuning, a version of this search could be used in high-volume scenarios, + such as scraping, crawling, application DDOS, credit-card testing, account takeover, + etc. Common data sources used for this detection are customized Apache logs, customized + IIS, and Splunk Stream. +type: ESCU +references: +- https://en.wikipedia.org/wiki/Session_ID +- https://en.wikipedia.org/wiki/Session_(computer_science) +- https://en.wikipedia.org/wiki/HTTP_cookie +- https://splunkbase.splunk.com/app/1809/ +author: Jim Apger, Splunk +search: '`stream_http` http_content_type=text* | rex field=cookie "form_key=(?\w+)" + | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where + TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as + ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) + | `web_fraud___anomalous_user_clickspeed_filter`' +known_false_positives: As is common with many fraud-related searches, we are usually + looking to attribute risk or synthesize relevant context with loosly written detections + that simply detect anamoluous behavior. +tags: + analytics_story: + - Web Fraud Detection + mitre_attack_id: + - T1078 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 6 + nist: + - DE.AE + - DE.CM + security_domain: threat + asset_type: account diff --git a/detections/web_fraud___password_sharing_across_accounts.yml b/detections/web_fraud___password_sharing_across_accounts.yml new file mode 100644 index 0000000000..d04e6668fc --- /dev/null +++ b/detections/web_fraud___password_sharing_across_accounts.yml @@ -0,0 +1,35 @@ +name: Web Fraud - Password Sharing Across Accounts +id: 31337a1a-53b9-4e05-96e9-55c934cb71d3 +version: 1 +date: '2018-10-08' +description: This search is used to identify user accounts that share a common password. +how_to_implement: We need to start with a dataset that allows us to see the values + of usernames and passwords that users are submitting to the website hosting the + Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized + or hashed value of a password is acceptable and certainly preferable to a clear-text + password. Common data sources used for this detection are customized Apache logs, + customized IIS, and Splunk Stream. +type: ESCU +references: +- https://en.wikipedia.org/wiki/Session_ID +- https://en.wikipedia.org/wiki/Session_(computer_science) +- https://en.wikipedia.org/wiki/HTTP_cookie +- https://splunkbase.splunk.com/app/1809/ +author: Jim Apger, Splunk +search: '`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | + rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data + "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames + values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 + | `web_fraud___password_sharing_across_accounts_filter`' +known_false_positives: As is common with many fraud-related searches, we are usually + looking to attribute risk or synthesize relevant context with loosely written detections + that simply detect anamoluous behavior. +tags: + analytics_story: + - Web Fraud Detection + cis20: + - CIS 16 + nist: + - DE.DP + security_domain: threat + asset_type: account diff --git a/detections/web_servers_executing_suspicious_processes.yml b/detections/web_servers_executing_suspicious_processes.yml index a07602837a..9c37ecd7fb 100644 --- a/detections/web_servers_executing_suspicious_processes.yml +++ b/detections/web_servers_executing_suspicious_processes.yml @@ -1,108 +1,36 @@ -asset_type: Web Server -confidence: medium -creation_date: '2017-03-14' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften +name: Web Servers Executing Suspicious Processes +id: ec3b7601-689a-4463-94e0-c9f45638efb9 +version: 1 +date: '2019-04-01' description: This search looks for suspicious processes on all systems labeled as web servers. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for suspicious processes on all systems - labeled as web servers - rule_title: Web Servers Executing Suspicious Processes - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 75 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" - AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" - OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") - by Processes.process Processes.process_name, Processes.dest Processes.user| - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' - suppress: - suppress_fields: dest, process - suppress_period: 28800s -eli5: This detection search uses the Enterprise Security Endpoint data model. The - search uses tstats to search within an accelerated data model to find suspicious - applications or processes such as whoami, ping, iptables, wget, service, or curl, - running on hosts which are marked as web servers in the Assets and Identity Framework - of ES. -entities: - - dest how_to_implement: You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. -id: ec3b7601-689a-4463-94e0-c9f45638efb9 -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89 - name: Investigate Suspicious Strings in HTTP Header - type: splunk +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" + AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" + OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") + by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`' known_false_positives: Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -mappings: - cis20: - - CIS 3 +tags: + analytics_story: + - Apache Struts Vulnerability + mitre_attack_id: + - T1082 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Exploitation of Vulnerability - - Execution - - Discovery - - System Information Discovery + - Actions on Objectives + cis20: + - CIS 3 nist: - - PR.IP -modification_date: '2019-04-01' -name: Web Servers Executing Suspicious Processes -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' + - PR.IP + security_domain: endpoint + asset_type: Web Server diff --git a/detections/windows_event_log_cleared.yml b/detections/windows_event_log_cleared.yml index a36d4cb362..ee1b44198c 100644 --- a/detections/windows_event_log_cleared.yml +++ b/detections/windows_event_log_cleared.yml @@ -1,108 +1,37 @@ -asset_type: Endpoint -confidence: high -creation_date: '2017-02-17' -data_metadata: - data_eventtypes: - - wineventlog_security - - wineventlog_system - data_source: - - Microsoft Windows - providing_technologies: - - Microsoft Windows +name: Windows Event Log Cleared +id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a +version: 3 +date: '2019-02-27' description: This search looks for Windows events that indicate one of the Windows event logs has been purged. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: The Event Logging System has been cleared or shutdown on - $dest$ - rule_title: Windows Event Log Cleared on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 60 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: ((eventtype=wineventlog_security) AND (signature_id=1102 OR signature_id=1100)) - OR ((eventtype=wineventlog_system) AND signature_id=104) | stats count min(_time) - as firstTime max(_time) as lastTime by signature_id dest user| `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - suppress: - suppress_fields: dest, signature_id - suppress_period: 28800s -eli5: This search looks at the Windows security and system event logs. EventCode 1002 - in the security log indicates that the log has been cleared, EventCode 1000 in the - security log indicates the event logging service has been shut down, and EventCode - 104 in the system log indicates the application log has been cleared. If any of - these events are found, a notable will be generated. -entities: - - dest how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. -id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a -investigations: - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 - name: Get Vulnerability Logs For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk - - id: fecf2918-670d-4f1c-872b-3d7317a41bf9 - name: Get Parent Process Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: ((`wineventlog_security` AND (signature_id=1102 OR signature_id=1100)) OR + ((`wineventlog_system` AND signature_id=104) | stats count min(_time) as firstTime + max(_time) as lastTime by signature_id dest user| `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` known_false_positives: It is possible that these logs may be legitimately cleared by Administrators. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - - CIS 6 +tags: + analytics_story: + - Windows Log Manipulation + - Ransomware + mitre_attack_id: + - T1070 kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Defense Evasion - - Indicator Removal on Host + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + - CIS 6 nist: - - DE.DP - - PR.IP - - PR.AC - - PR.AT - - DE.AE -modification_date: '2019-02-27' -name: Windows Event Log Cleared -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' + - DE.DP + - PR.IP + - PR.AC + - PR.AT + - DE.AE + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/windows_hosts_file_modification.yml b/detections/windows_hosts_file_modification.yml new file mode 100644 index 0000000000..42c08a9ce3 --- /dev/null +++ b/detections/windows_hosts_file_modification.yml @@ -0,0 +1,39 @@ +name: Windows hosts file modification +id: 06a6fc63-a72d-41dc-8736-7e3dd9612116 +version: 1 +date: '2018-11-02' +description: The search looks for modifications to the hosts file on all Windows endpoints + across your environment. +how_to_implement: To successfully implement this search, you must be ingesting data + that records the file-system activity from your hosts to populate the Endpoint.Filesystem + data model node. This is typically populated via endpoint detection-and-response + products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. + The data used for this search is typically generated via logs that report file-system + reads and writes. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path + Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* + | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter`' +known_false_positives: There may be legitimate reasons for system administrators to + add entries to this file. +tags: + analytics_story: + - Host Redirection + kill_chain_phases: + - Command and Control + cis20: + - CIS 3 + - CIS 8 + - CIS 12 + nist: + - PR.IP + - PR.PT + - PR.AC + - DE.AE + - DE.CM + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/wmi_perm_event_subscription.yml b/detections/wmi_perm_event_subscription.yml deleted file mode 100644 index 2538d0e370..0000000000 --- a/detections/wmi_perm_event_subscription.yml +++ /dev/null @@ -1,105 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-10-23' -data_metadata: - data_source: - - Microsoft Windows - data_sourcetypes: - - WinEventLog://Microsoft-Windows-WMI-Activity/Operational - providing_technologies: - - Microsoft Windows -description: This search looks for the creation of WMI permanent event subscriptions. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest - rule_description: This search looks for the creation of a permanent WMI event - subscription via Windows event logs. - rule_title: WMI Event Subscription Detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" - EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" - | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | - stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, - consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName - as dest - suppress: - suppress_fields: dest - suppress_period: 28800s -eli5: Attackers are increasingly abusing Windows Management Infrastructure (WMI) for - stealth, persistence, lateral movement, or just to leverage its functionality. This - search looks for the creation of a WMI event subscription by watching for Windows - event ID 5861. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting the - Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on - the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Although unlikely, administrators may use event subscriptions - for legitimate purposes. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - - Persistence - - Windows Management Instrumentation Event Subscription - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2018-10-23' -name: WMI Permanent Event Subscription -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/wmi_perm_event_subscription_sysmon.yml b/detections/wmi_perm_event_subscription_sysmon.yml deleted file mode 100644 index 91c8543eb0..0000000000 --- a/detections/wmi_perm_event_subscription_sysmon.yml +++ /dev/null @@ -1,103 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-10-23' -data_metadata: - data_source: - - Microsoft Windows - data_sourcetypes: - - WinEventLog://Microsoft-Windows-WMI-Activity/Operational - providing_technologies: - - Microsoft Windows -description: This search looks for the creation of WMI permanent event subscriptions. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user - rule_description: This search looks for the creation of a permanent WMI event - subscription via Sysmon logs. - rule_title: WMI Event Subscription Detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode=21 - | rename host as dest | table _time, dest, user, Operation, EventType, Query, - Consumer, Filter - suppress: - suppress_fields: dest,user - suppress_period: 28800s -eli5: Attackers are increasingly abusing Windows Management Infrastructure (WMI) for - stealth, persistence, lateral movement, or just to leverage its functionality. This - search looks for the creation of a WMI event subscription by watching for Sysmon - event ID 21. -entities: - - dest -how_to_implement: To successfully implement this search, you must be collecting Sysmon - data using Sysmon version 6.1 or greater and have Sysmon configured to generate - alerts for WMI activity. In addition, you must have at least version 6.0.4 of the - Sysmon TA installed to properly parse the fields. -id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9 -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Although unlikely, administrators may use event subscriptions - for legitimate purposes. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - - Persistence - - Windows Management Instrumentation Event Subscription - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2018-10-23' -name: WMI Permanent Event Subscription - Sysmon -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/wmi_permanent_event_subscription.yml b/detections/wmi_permanent_event_subscription.yml new file mode 100644 index 0000000000..064e32a2c4 --- /dev/null +++ b/detections/wmi_permanent_event_subscription.yml @@ -0,0 +1,36 @@ +name: WMI Permanent Event Subscription +id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d +version: 1 +date: '2018-10-23' +description: This search looks for the creation of WMI permanent event subscriptions. +how_to_implement: To successfully implement this search, you must be ingesting the + Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on + the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" + | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats + count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, + Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`' +known_false_positives: Although unlikely, administrators may use event subscriptions + for legitimate purposes. +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 + - T1084 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/wmi_permanent_event_subscription___sysmon.yml b/detections/wmi_permanent_event_subscription___sysmon.yml new file mode 100644 index 0000000000..5462c67c41 --- /dev/null +++ b/detections/wmi_permanent_event_subscription___sysmon.yml @@ -0,0 +1,34 @@ +name: WMI Permanent Event Subscription - Sysmon +id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9 +version: 1 +date: '2018-10-23' +description: This search looks for the creation of WMI permanent event subscriptions. +how_to_implement: To successfully implement this search, you must be collecting Sysmon + data using Sysmon version 6.1 or greater and have Sysmon configured to generate + alerts for WMI activity. In addition, you must have at least version 6.0.4 of the + Sysmon TA installed to properly parse the fields. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, + EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`' +known_false_positives: Although unlikely, administrators may use event subscriptions + for legitimate purposes. +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 + - T1084 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/wmi_process_launch.yml b/detections/wmi_process_launch.yml deleted file mode 100644 index 91f82dea31..0000000000 --- a/detections/wmi_process_launch.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-10-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - Sysmon - - Tanium - - Ziften -description: This search looks for processes launched via WMI. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for child processes of WmiPrvSE.exe, which - indicates that a process was launched via WMI. - rule_title: Process launched via WMI on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - macros: - - wmi_process_launch_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes - where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest - Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)`| `wmi_process_launch_filter`' - suppress: - suppress_fields: dest, user - suppress_period: 28800s -eli5: Attackers are increasingly abusing Windows Management Infrastructure (WMI) for - stealth, persistence, lateral movement, or just to leverage its functionality. This - search looks for processes launched via WMI, either remotely or locally, by looking - for processes launched by WmiPrvSE.exe, which is the process WMI uses to execute - new processes and commands. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: 24869767-8579-485d-9a4f-d9ddfd8f0cac -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Although unlikely, administrators may use wmi to execute commands - for legitimate purposes. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2020-03-16' -name: Process Execution via WMI -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/wmi_script_execution.yml b/detections/wmi_script_execution.yml deleted file mode 100644 index 6af5245b33..0000000000 --- a/detections/wmi_script_execution.yml +++ /dev/null @@ -1,109 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-10-23' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - Sysmon - - Tanium - - Ziften -description: This search looks for scripts launched via WMI. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for scrcons.exe, which indicates that - a script was launched via WMI. - rule_title: Script execution via WMI on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - macros: - - wmi_script_execution_filter - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: '| tstats `security_content_summariesonly` count values(Processes.process) as process - min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes - where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest - Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| - `security_content_ctime(lastTime)`| `wmi_script_execution_filter`' - suppress: - suppress_fields: dest,process - suppress_period: 28800s -eli5: Attackers are increasingly abusing Windows Management Infrastructure for stealth, - persistence, lateral movement, or just to leverage its functionality. This search - looks for scripts launched via WMI, either remotely or locally, by looking for the - execution of scrcons.exe, which is the scripting host used by WMI, similar to wscript - or cscript. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: aa73f80d-d728-4077-b226-81ea0c8be589 -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Although unlikely, administrators may use wmi to launch scripts - for legitimate purposes. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2020-03-16' -name: Script Execution via WMI -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '3.0' diff --git a/detections/wmi_temp_event_subscription.yml b/detections/wmi_temp_event_subscription.yml deleted file mode 100644 index 7501e676c1..0000000000 --- a/detections/wmi_temp_event_subscription.yml +++ /dev/null @@ -1,108 +0,0 @@ -asset_type: Endpoint -confidence: medium -creation_date: '2018-10-23' -data_metadata: - data_source: - - Microsoft Windows - data_sourcetypes: - - WinEventLog://Microsoft-Windows-WMI-Activity/Operational - providing_technologies: - - Microsoft Windows -description: This search looks for the creation of WMI temporary event subscriptions. -detect: - splunk: - correlation_rule: - notable: - nes_fields: dest, user, process - rule_description: This search looks for the creation of a temporary WMI event - subscription via Windows event logs. - rule_title: Temporary WMI Event Subscription Detected on $dest$ - risk: - risk_object: dest - risk_object_type: - - system - risk_score: 70 - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" - EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" - | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = - 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE - TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' - OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime - max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` - suppress: - suppress_fields: dest,user - suppress_period: 28800s -eli5: Attackers are increasingly abusing Windows Management Infrastructure (WMI) for - stealth, persistence, lateral movement, or just to leverage its functionality. This - search looks for the creation of a WMI temporary event subscription by watching - for Windows event ID 5860. -entities: - - dest -how_to_implement: To successfully implement this search, you must be ingesting the - Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on - the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83 -investigations: - - id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 - name: Get Sysmon WMI Activity for Host - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 - name: Get Authentication Logs For Endpoint - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d55 - name: Get Risk Modifiers For User - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 - name: Get Process Info - type: splunk - - id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 - name: Get Notable History - type: splunk - - id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 - name: Get Notable Info - type: splunk - - id: fdcfb369-1725-4c24-824a-22972d7f0d65 - name: Get Risk Modifiers For Endpoint - type: splunk - - id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 - name: Get User Information from Identity Table - type: splunk -known_false_positives: Some software may create WMI temporary event subscriptions - for various purposes. The included search contains an exception for two of these - that occur by default on Windows 10 systems. You may need to modify the search to - create exceptions for other legitimate events. -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -mappings: - cis20: - - CIS 3 - - CIS 5 - kill_chain_phases: - - Actions on Objectives - mitre_attack: - - Execution - - Windows Management Instrumentation - - Persistence - - Windows Management Instrumentation Event Subscription - nist: - - PR.PT - - PR.AT - - PR.AC - - PR.IP -modification_date: '2018-10-23' -name: WMI Temporary Event Subscription -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: [] -security_domain: endpoint -spec_version: 2 -type: splunk -version: '1.0' diff --git a/detections/wmi_temporary_event_subscription.yml b/detections/wmi_temporary_event_subscription.yml new file mode 100644 index 0000000000..e0973069d6 --- /dev/null +++ b/detections/wmi_temporary_event_subscription.yml @@ -0,0 +1,39 @@ +name: WMI Temporary Event Subscription +id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83 +version: 1 +date: '2018-10-23' +description: This search looks for the creation of WMI temporary event subscriptions. +how_to_implement: To successfully implement this search, you must be ingesting the + Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on + the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +type: ESCU +references: [] +author: Rico Valdez, Splunk +search: '`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" + | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = ''wsmprovhost.exe''" + AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA ''AntiVirusProduct'' + OR TargetInstance ISA ''FirewallProduct'' OR TargetInstance ISA ''AntiSpywareProduct''" + | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | + `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`' +known_false_positives: Some software may create WMI temporary event subscriptions + for various purposes. The included search contains an exception for two of these + that occur by default on Windows 10 systems. You may need to modify the search to + create exceptions for other legitimate events. +tags: + analytics_story: + - Suspicious WMI Use + mitre_attack_id: + - T1047 + - T1084 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 5 + nist: + - PR.PT + - PR.AT + - PR.AC + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/docs/baselines.spec.json b/docs/baselines.spec.json deleted file mode 100644 index ed0d1604c5..0000000000 --- a/docs/baselines.spec.json +++ /dev/null @@ -1,472 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/baselines.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the Phantom server. Currently, we ship these values as automation (hostname) and encourage users to modify them according to their environments: for example, automation (hostname)", - "examples": [ - "automation (hostname)" - ], - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on the Phantom community repository on github, with underscores, and appended with \"community/.\" The playbooks are hosted on https://github.com/phantomcyber/playbooks. For example: community/simple_network_enrichment.", - "examples": [ - "community/dns_hijack_investigation" - ], - "type": "string" - }, - "playbook_url": { - "description": "URL of the playbook on the Phantom website", - "examples": [ - "https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" - ], - "type": "string" - }, - "schedule": { - "description": "Various fields used to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber, or Red)", - "examples": [ - "green" - ], - "type": "string" - }, - "severity": { - "description": "Severity in Phantom (High, Medium, Low)", - "examples": [ - "medium" - ], - "type": "string" - } - }, - "required": [ - "phantom_server", - "playbook_name", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "properties": { - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within Splunk Enterprise for investigation", - "examples": [ - "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)" - ], - "type": "string" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - } - }, - "description": "The fields that make up the manifest of a version 2 baseline search", - "properties": { - "baseline": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - } - }, - "type": "object" - }, - "creation_date": { - "description": "The date the baseline manifest was created", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "data_metadata": { - "description": "Information about the data being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "examples": [ - "wineventlog" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "examples": [ - "DNS" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "examples": [ - "stream:dns" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "Passive Total", - "WHOIS" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the search is is doing to create a baseline", - "examples": [ - "The search takes corporate and common cloud-provider domains configured under \"cim_corporate_email_domains.csv,\" \"cim_corporate_web_domains.csv,\" and \"cloud_domains.csv,\" finds their responses across the last 30 days from data in the \"Network_Traffic\" data model, then stores the output under the \"discovered_dns_records.csv\" lookup." - ], - "type": "string" - }, - "eli5": { - "description": "Explain it Like I'm 5: A detailed description of the SPL in the search, written in a style that can be understood by a future Splunk expert.", - "examples": [ - "Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named \"discovered_dns_records.csv.\"" - ], - "type": "string" - }, - "entities": { - "description": "A list of entities that will be used in the story flow or that are relevant to the security investigation.", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query", - "answer" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion of how to implement this search, from what needs to be ingested, config files modified, and suggested per-site modifications", - "examples": [ - "To successfully implement this search, you must be ingesting DNS logs and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation." - ], - "type": "string" - }, - "id": { - "description": "The unique identifier for the search", - "examples": [ - "c096f721-8842-42ce-bfc7-74bd8c72b7c3" - ], - "type": "string" - }, - "known_false_positives": { - "description": "Describe the known false postives while the analyst builds the baseline.", - "examples": [ - "Please vet the lookup created by this baseline search." - ], - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "items": { - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "name": { - "description": "The name of the search that creates the baseline", - "examples": [ - "Discover DNS records" - ], - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "spec_version": { - "description": "The version of the detection search specification this manifest follows", - "examples": [ - "2.0" - ], - "type": "integer" - }, - "type": { - "description": "The type of baseline", - "enum": [ - "splunk", - "phantom", - "uba" - ], - "examples": [ - "splunk" - ] - }, - "version": { - "description": "The version of the search", - "examples": [ - "1", - "2" - ], - "type": "string" - } - }, - "required": [ - "id", - "creation_date", - "data_metadata", - "eli5", - "known_false_positives", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "name", - "description", - "type", - "spec_version", - "version", - "baseline" - ], - "title": "Baseline Manifest", - "type": "object" -} diff --git a/docs/baselines.spec.md b/docs/baselines.spec.md deleted file mode 100644 index 3ed4dbead0..0000000000 --- a/docs/baselines.spec.md +++ /dev/null @@ -1,1345 +0,0 @@ - -# Baseline Manifest Schema - -``` -https://api.splunkresearch.com/schemas/baselines.json -``` - -The fields that make up the manifest of a version 2 baseline search - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Permitted | | - -# Baseline Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [baseline](#baseline) | `object` | **Required** | No | Baseline Manifest (this schema) | -| [creation_date](#creation_date) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Baseline Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [eli5](#eli5) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Baseline Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [known_false_positives](#known_false_positives) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Baseline Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [name](#name) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Baseline Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | **Required** | No | Baseline Manifest (this schema) | -| [type](#type) | `enum` | **Required** | No | Baseline Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Baseline Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## baseline - - -`baseline` - -* is **required** -* type: `object` -* defined in this schema - -### baseline Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `phantom`| | Optional | -| `splunk`| | Optional | - - - -#### phantom - - -`phantom` - -* is optional -* type: reference - -##### phantom Type - - -* []() – `#/definitions/phantom` - - - - - - - -#### splunk - - -`splunk` - -* is optional -* type: reference - -##### splunk Type - - -* []() – `#/definitions/splunk` - - - - - - - - - - -## creation_date - -The date the baseline manifest was created - -`creation_date` - -* is **required** -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - -### creation_date Example - -```json -"2019-02-14" -``` - - -## data_metadata - -Information about the date being ingested - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_eventtypes Example - -```json -wineventlog -``` - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Cloud_Infrastructure": "", - "Change": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### data_models Example - -```json -Network_Resolution -``` - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_source Example - -```json -DNS -``` - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_sourcetypes Example - -```json -stream:dns -``` - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "Passive Total", - "WHOIS" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "", - "Censys": "", - "Passive Total": "", - "WHOIS": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### providing_technologies Example - -```json -Bro -``` - - - - - - - -## description - -A description of what the search is is doing to create a baseline - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - -### description Example - -```json -"The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Traffic` datamodel, then stores the output under the `discovered_dns_records.csv` lookup" -``` - - -## eli5 - -Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert - -`eli5` - -* is **required** -* type: `string` -* defined in this schema - -### eli5 Type - - -`string` - - - - - - -### eli5 Example - -```json -"Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`" -``` - - -## entities - -A list of entities that will used in the story flow or are relevant to the security investigation. - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that will used in the story flow or are relevant to the security investigation.", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query", - "answer" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "", - "query": "", - "answer": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -### entities Examples - -```json -"dest" -``` - -```json -"user" -``` - - - -## how_to_implement - -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications - -`how_to_implement` - -* is **required** -* type: `string` -* defined in this schema - -### how_to_implement Type - - -`string` - - - - - - -### how_to_implement Example - -```json -"To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation" -``` - - -## id - -The unique identifier for the search - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - -### id Example - -```json -"c096f721-8842-42ce-bfc7-74bd8c72b7c3" -``` - - -## known_false_positives - -Describe the known false postives while the analyst builds the baseline. - -`known_false_positives` - -* is **required** -* type: `string` -* defined in this schema - -### known_false_positives Type - - -`string` - - - - - - -### known_false_positives Example - -```json -"Please vet the lookup created by this baseline search." -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2019-02-14" -``` - - -## name - -The name of the search that creates the baseline - -`name` - -* is **required** -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - -### name Example - -```json -"Discover DNS records" -``` - - -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## spec_version - -The version of the detection search specification this manifest follows - -`spec_version` - -* is **required** -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -"2.0" -``` - - -## type - -The type of baseline - -`type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#type-known-values). - -### type Known Values -| Value | Description | -|-------|-------------| -| `splunk` | | -| `phantom` | | -| `uba` | | - - - -### type Example - -```json -"splunk" -``` - - -## version - -The version of the search - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - -### version Examples - -```json -"1" -``` - -```json -"2" -``` - - - -# Baseline Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [schedule](#schedule) | `object` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/splunk` | -| [search](#search) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/splunk` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - -### phantom_server Example - -```json -"automation (hostname)" -``` - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - -### playbook_name Example - -```json -"community/dns_hijack_investigation" -``` - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - -### playbook_url Example - -```json -"https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" -``` - - -## schedule - -Various fields to assist in scheduling the search - -`schedule` - -* is optional -* type: `object` -* defined in this schema - -### schedule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cron_schedule`| string | Optional | -| `earliest_time`| string | Optional | -| `latest_time`| string | Optional | - - - -#### cron_schedule - -Schedule of the search in cron format - -`cron_schedule` - -* is optional -* type: `string` - -##### cron_schedule Type - - -`string` - - - - - - -##### cron_schedule Example - -```json -0 * * * * -``` - - - - -#### earliest_time - -The earliest time the search should run in Splunk format - -`earliest_time` - -* is optional -* type: `string` - -##### earliest_time Type - - -`string` - - - - - - -##### earliest_time Example - -```json --70m@m -``` - - - - -#### latest_time - -The latest time tes search should run against in Splunk format - -`latest_time` - -* is optional -* type: `string` - -##### latest_time Type - - -`string` - - - - - - -##### latest_time Example - -```json --10m@m -``` - - - - - - - -## search - -The search (in SPL) executed within core Splunk for investgation. - -`search` - -* is optional -* type: `string` -* defined in this schema - -### search Type - - -`string` - - - - - - -### search Example - -```json -"| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)" -``` - - -## sensitivity - -TLP colors (White, Green, Amber or Red) - -`sensitivity` - -* is optional -* type: `string` -* defined in this schema - -### sensitivity Type - - -`string` - - - - - - -### sensitivity Example - -```json -"green" -``` - - -## severity - -Severity in phantom (High, Medium, Low) - -`severity` - -* is optional -* type: `string` -* defined in this schema - -### severity Type - - -`string` - - - - - - -### severity Example - -```json -"medium" -``` - diff --git a/docs/detections.spec.json b/docs/detections.spec.json deleted file mode 100644 index 4f4f9250b7..0000000000 --- a/docs/detections.spec.json +++ /dev/null @@ -1,1213 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/detections.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "examples": [ - "src" - ], - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "examples": [ - "The table represents a list of DNS records and their responses for recently changed corporate domains." - ], - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review. Add tokenized fieldnames.", - "examples": [ - "DNS record changed on $src$" - ], - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "The field to which you are assigning risk", - "examples": [ - "src" - ], - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "examples": [ - "system" - ], - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "examples": [ - "40" - ], - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search that will trigger the Phantom playbook", - "examples": [ - "| sendalert runphantomplaybook" - ], - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "examples": [ - "src" - ], - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "examples": [ - "28800s" - ], - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "phantom_server": { - "description": "IP address and username of the Phantom server. Currently, we ship these values as automation (hostname) and encourage users to modify them according to their environments: for example, automation (hostname)", - "examples": [ - "automation (hostname)" - ], - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on the Phantom community repository on github, with underscores, and appended with \"community/.\" The playbooks are hosted on https://github.com/phantomcyber/playbooks. For example: community/simple_network_enrichment", - "examples": [ - "community/dns_hijack_detect_playbook" - ], - "type": "string" - }, - "playbook_url": { - "description": "URL of the playbook on the Phantom website", - "examples": [ - "https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" - ], - "type": "string" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber, Red)", - "examples": [ - "green" - ], - "type": "string" - }, - "severity": { - "description": "Severity in Phantom (High, Medium, Low)", - "examples": [ - "high" - ], - "type": "string" - } - }, - "required": [ - "correlation_rule", - "playbook_name", - "phantom_server", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review", - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "TThe field to which you are assigning risk", - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search that will trigger the Phantom playbook", - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "type": "object" - }, - "uba": { - "properties": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review", - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "The field to which you are assigning risk", - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within Splunk Enterprise", - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "event_type": { - "description": "An anomaly or threat", - "examples": [ - "anomaly" - ], - "type": "string" - }, - "model": { - "description": "The name of the Splunk UBA model that detected the anomaly.", - "examples": [ - "UBA Model" - ], - "type": "string" - }, - "model_version": { - "description": "URL of the playbook on the Phantom website", - "examples": [ - "2.0" - ], - "type": "string" - }, - "threat_category": { - "description": "The category of a threat in Splunk UBA", - "examples": [ - "Malware" - ], - "type": "string" - } - }, - "required": [ - "correlation_rule", - "event_type", - "model", - "model_version", - "threat_category" - ], - "type": "object" - } - }, - "description": "An object that defines the parameters for detecting things using various Splunk capabilities", - "properties": { - "asset_type": { - "description": "Designates the type of asset being investigated", - "examples": [ - "Endpoint" - ], - "type": "string" - }, - "baselines": { - "description": "An array of the baseline objects to execute before the detection ", - "items": { - "properties": { - "id": { - "description": "UUID of the baseline object", - "examples": [ - "c096f721-8842-42ce-bfc7-74bd8c72b7c3" - ], - "type": "string" - }, - "name": { - "description": "Name of the baseline object", - "examples": [ - "Discover DNS records" - ], - "type": "string" - }, - "type": { - "description": "Type of baseline to execute", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "splunk" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "confidence": { - "description": "Confidence that detected behavior is malicious", - "enum": [ - "high", - "medium", - "low" - ], - "examples": [ - "high" - ] - }, - "creation_date": { - "description": "The date the story manifest was created", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "data_metadata": { - "description": "Information about the data being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "examples": [ - "wineventlog" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "examples": [ - "DNS" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "examples": [ - "stream:dns" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the detection is designed to find", - "examples": [ - "The search takes the DNS records and the results of the discovered_dns_records lookup and determines whether any records have changed. It does this by searching DNS response from the Network_Resolution data model within the last day." - ], - "type": "string" - }, - "detect": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - }, - { - "required": [ - "uba" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - }, - "uba": { - "$ref": "#/definitions/uba" - } - }, - "type": "object" - }, - "eli5": { - "description": "Explain it Like I'm 5: A detailed description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "examples": [ - "Using a lookup \"discover_dns_records\" generated by support search \"Discover DNS records,\" we check previous network traffic and make sure the responses have not changed." - ], - "type": "string" - }, - "entities": { - "description": "A list of entities outputted by the search", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion of how to implement this search, including what needs to be ingested, the config files modified, and suggested per-site modifications", - "examples": [], - "type": "string" - }, - "id": { - "description": "The unique identifier for the detection", - "examples": [ - "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that the \"discover_dns_record\" lookup table be populated by the included support search \"Discover DNS record.\"" - ], - "type": "string" - }, - "investigations": { - "description": "An array of the investigation objects to execute on the detection results ", - "items": { - "properties": { - "id": { - "description": "UUID of the investigation object", - "examples": [ - "bc11a8cf-35e7-4bb2-8140-e756cc06fd72" - ], - "type": "string" - }, - "name": { - "description": "Name of the investigation object ", - "examples": [ - "Get DNS Server History for a Host" - ], - "type": "string" - }, - "type": { - "description": "Type of investigation object", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "splunk" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "known_false_positives": { - "description": "Scenarios in which detected behavior is benign, coupled with suggestions on how to verify the behavior", - "examples": [ - "Legitimate DNS changes can be detected in this search. Investigate, verify, and update the list of provided current answers for the domains in question, as appropriate." - ], - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story", - "items": { - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "mappings": { - "description": "Mappings to various industry standards and frameworks", - "properties": { - "cis20": { - "description": "A list of critical security controls this search helps you implement", - "examples": [ - "CIS 12" - ], - "items": { - "enum": [ - "CIS 1", - "CIS 2", - "CIS 3", - "CIS 4", - "CIS 5", - "CIS 6", - "CIS 7", - "CIS 8", - "CIS 9", - "CIS 10", - "CIS 11", - "CIS 12", - "CIS 13", - "CIS 14", - "CIS 15", - "CIS 16", - "CIS 17", - "CIS 18", - "CIS 19", - "CIS 20" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "emoji": { - "description": "A list of security emojis that will help UBA understand this alert as an external alarm", - "examples": [ - "EndPoint" - ], - "items": { - "enum": [ - "EndPoint", - "AD", - "Firewall", - "ApplicationLog", - "IPS", - "CloudData", - "Correlation", - "Printer", - "Badge" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "kill_chain_phases": { - "description": "A list of kill-chain phases to which the search applies", - "examples": [ - "Reconnaissance" - ], - "items": { - "enum": [ - "Reconnaissance", - "Weaponization", - "Delivery", - "Exploitation", - "Installation", - "Command and Control", - "Actions on Objectives" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "mitre_attack": { - "description": "A list of the techniques and tactics identified by the search", - "examples": [ - "Defense Evasion", - "Initial Access" - ], - "items": { - "enum": [ - "Initial Access", - "Execution", - "Persistence", - "Spearphishing Attachment", - "Spearphishing Link", - "Privilege Escalation", - "Defense Evasion", - "Credential Access", - "Discovery", - "Lateral Movement", - "Collection", - "Exfiltration", - "Command and Control", - "Command and Control Protocol", - "Commonly Used Port", - "Custom Cryptographic Protocol", - "DLL Injection", - "DLL Search Order Hijacking", - "DLL Side-Loading", - "Data Compressed", - "Data Encrypted", - "Data Obfuscation", - "Data Staged", - "Data Transfer Size Limits", - "Data from Local System", - "Data from Network Shared Drive", - "Data from Removable Media", - "Disabling Security Tools", - "Email Collection", - "Execution through API", - "Exfiltration Over Alternative Protocol", - "Exfiltration Over Command and Control Channel", - "Exfiltration Over Other Network Medium", - "Exfiltration Over Physical Medium", - "Exploitation of Vulnerability", - "Fallback Channels", - "File Deletion", - "File System Logical Offsets", - "File System Permissions Weakness", - "File and Directory Discovery", - "Graphical User Interface", - "Hypervisor", - "Indicator Blocking", - "Indicator Removal from Tools", - "Indicator Removal on Host", - "Input Capture", - "InstallUtil", - "Legitimate Credentials", - "Local Network Configuration Discovery", - "Local Network Connections Discovery", - "Local Port Monitor", - "Logon Scripts", - "MSBuild", - "Masquerading", - "Modify Existing Service", - "Modify Registry", - "Multi-Stage Channels", - "Multiband Communication", - "Multilayer Encryption", - "NTFS Extended Attributes", - "Network Service Scanning", - "Network Share Connection Removal", - "Network Sniffing", - "New Service", - "Obfuscated Files or Information", - "Pass the Hash", - "Pass the Ticket", - "Path Interception", - "Peripheral Device Discovery", - "Permission Groups Discovery", - "PowerShell", - "Process Discovery", - "Process Hollowing", - "Query Registry", - "Redundant Access", - "Registry Run Keys / Start Folder", - "Regsvcs/Regasm", - "Regsvr32", - "Remote Desktop Protocol", - "Create Account", - "Remote File Copy", - "Remote Services", - "Remote System Discovery", - "Replication Through Removable Media", - "Rootkit", - "Rundll32", - "Scheduled Task", - "Scheduled Transfer", - "Screen Capture", - "Scripting", - "Security Software Discovery", - "Security Support Provider", - "Service Execution", - "Service Registry Permissions Weakness", - "Shared Webroot", - "Shortcut Modification", - "Software Packing", - "Standard Application Layer Protocol", - "Standard Cryptographic Protocol", - "Standard Non-Application Layer Protocol", - "System Information Discovery", - "System Owner/User Discovery", - "System Service Discovery", - "System Time Discovery", - "Taint Shared Content", - "Third-party Software", - "Timestomp", - "Two-Factor Authentication Interception", - "Uncommonly Used Port", - "Video Capture", - "Valid Accounts", - "Web Service", - "Web Shell", - "Windows Admin Shares", - "Windows Management Instrumentation Event Subscription", - "Windows Management Instrumentation", - "Windows Remote Management", - "Winlogon Helper DLL", - "Exploitation for Privilege Escalation" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "nist": { - "description": "A list of the NIST controls the search helps you implement", - "examples": [ - "ID.AM", - "PR.PT" - ], - "items": { - "enum": [ - "ID.AM", - "ID.RA", - "PR.DS", - "PR.IP", - "PR.AC", - "PR.PT", - "PR.AT", - "PR.MA", - "DE.CM", - "DE.DP", - "DE.AE", - "RS.MI", - "RS.AN", - "RS.RP", - "RS.IM", - "RS.CO", - "RC.IM", - "RC.CO" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "type": "object" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "name": { - "description": "The name of the detection", - "examples": [ - "DNS record changed" - ], - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "references": { - "description": "A list of URLs that give more information about the search", - "examples": [ - "https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "responses": { - "description": "An array of the response objects to exectute on the detection results", - "items": { - "properties": { - "id": { - "description": "UUID of the response object", - "examples": [ - "1169w17b-ef78-4b59-aae8-5369073014e1" - ], - "type": "string" - }, - "name": { - "description": "Name of response object", - "examples": [ - "DNS hijack reponse" - ], - "type": "string" - }, - "type": { - "description": "Type of baseline to execute", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "phantom" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "security_domain": { - "description": "The high-level security area to which the search belongs", - "enum": [ - "access", - "endpoint", - "network", - "threat" - ], - "examples": [ - "endpoint" - ] - }, - "spec_version": { - "description": "The version of the detection specification this manifest follows", - "examples": [ - "2.0" - ], - "type": "integer" - }, - "type": { - "description": "The type of detection", - "enum": [ - "uba", - "splunk", - "phantom" - ], - "examples": [ - "phantom" - ] - }, - "version": { - "description": "The version of the detection", - "examples": [ - "1" - ], - "type": "string" - } - }, - "required": [ - "confidence", - "creation_date", - "data_metadata", - "eli5", - "how_to_implement", - "known_false_positives", - "maintainers", - "modification_date", - "original_authors", - "description", - "id", - "type", - "security_domain", - "version", - "detect" - ], - "title": "Detection Manifest", - "type": "object" -} diff --git a/docs/detections.spec.md b/docs/detections.spec.md deleted file mode 100644 index 6fdfd19620..0000000000 --- a/docs/detections.spec.md +++ /dev/null @@ -1,2855 +0,0 @@ - -# Detection Manifest Schema - -``` -https://api.splunkresearch.com/schemas/detections.json -``` - -A object that defines the parameters for detecting things using various Splunk capabilities - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Permitted | | - -# Detection Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [asset_type](#asset_type) | `string` | Optional | No | Detection Manifest (this schema) | -| [baselines](#baselines) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [confidence](#confidence) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [creation_date](#creation_date) | `string` | **Required** | No | Detection Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Detection Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Detection Manifest (this schema) | -| [detect](#detect) | `object` | **Required** | No | Detection Manifest (this schema) | -| [eli5](#eli5) | `string` | **Required** | No | Detection Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Detection Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Detection Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Detection Manifest (this schema) | -| [investigations](#investigations) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [known_false_positives](#known_false_positives) | `string` | **Required** | No | Detection Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Detection Manifest (this schema) | -| [mappings](#mappings) | `object` | Optional | No | Detection Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Detection Manifest (this schema) | -| [name](#name) | `string` | Optional | No | Detection Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Detection Manifest (this schema) | -| [references](#references) | `string[]` | Optional | No | Detection Manifest (this schema) | -| [responses](#responses) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [security_domain](#security_domain) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | Optional | No | Detection Manifest (this schema) | -| [type](#type) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Detection Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## asset_type - -Designates the type of asset being investigated - -`asset_type` - -* is optional -* type: `string` -* defined in this schema - -### asset_type Type - - -`string` - - - - - - -### asset_type Example - -```json -"Endpoint" -``` - - -## baselines - -An array of the baseline objects to exectute before the detection - -`baselines` - -* is optional -* type: `object[]` -* defined in this schema - -### baselines Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `type`| string | **Required** | - - - -#### id - -UUID of the baseline object - -`id` - -* is **required** -* type: `string` - -##### id Type - - -`string` - - - - - - -##### id Example - -```json -c096f721-8842-42ce-bfc7-74bd8c72b7c3 -``` - - - - -#### name - -name of baseline object - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Discover DNS records -``` - - - - -#### type - -Type of baseline to execute - -`type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#baselines-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### type Example - -```json -splunk -``` - - - - - - - - - -## confidence - -Confidence that detected behavior is malicious - -`confidence` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#confidence-known-values). - -### confidence Known Values -| Value | Description | -|-------|-------------| -| `high` | | -| `medium` | | -| `low` | | - - - -### confidence Example - -```json -"high" -``` - - -## creation_date - -The date the story manifest was created - -`creation_date` - -* is **required** -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - -### creation_date Example - -```json -"2019-02-14" -``` - - -## data_metadata - -Information about the date being ingested - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_eventtypes Example - -```json -wineventlog -``` - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Change": "", - "Cloud_Infrastructure": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### data_models Example - -```json -Network_Resolution -``` - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_source Example - -```json -DNS -``` - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_sourcetypes Example - -```json -stream:dns -``` - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "", - "OSquery": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### providing_technologies Example - -```json -Bro -``` - - - - - - - -## description - -A description of what the detection is designed to find - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - -### description Example - -```json -"The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day." -``` - - -## detect - - -`detect` - -* is **required** -* type: `object` -* defined in this schema - -### detect Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `phantom`| | Optional | -| `splunk`| | Optional | -| `uba`| | Optional | - - - -#### phantom - - -`phantom` - -* is optional -* type: reference - -##### phantom Type - - -* []() – `#/definitions/phantom` - - - - - - - -#### splunk - - -`splunk` - -* is optional -* type: reference - -##### splunk Type - - -* []() – `#/definitions/splunk` - - - - - - - -#### uba - - -`uba` - -* is optional -* type: reference - -##### uba Type - - -* []() – `#/definitions/uba` - - - - - - - - - - -## eli5 - -Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert - -`eli5` - -* is **required** -* type: `string` -* defined in this schema - -### eli5 Type - - -`string` - - - - - - -### eli5 Example - -```json -"Using a lookup `discover_dns_records` generated by support search \"Discover DNS records\" we check previous network traffic and make sure the responses have not changed." -``` - - -## entities - -A list of entities that is outputed by the search... - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that is outputed by the search...", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "", - "query": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -### entities Examples - -```json -"dest" -``` - -```json -"user" -``` - - - -## how_to_implement - -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications - -`how_to_implement` - -* is **required** -* type: `string` -* defined in this schema - -### how_to_implement Type - - -`string` - - - - - - - -## id - -The unique identifier for the detection - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - -### id Example - -```json -"To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search \"Discover DNS record\"." -``` - - -## investigations - -An array of the investigation objects to exectute on the detection results - -`investigations` - -* is optional -* type: `object[]` -* defined in this schema - -### investigations Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `type`| string | **Required** | - - - -#### id - -UUID of the investigation object - -`id` - -* is **required** -* type: `string` - -##### id Type - - -`string` - - - - - - -##### id Example - -```json -bc11a8cf-35e7-4bb2-8140-e756cc06fd72 -``` - - - - -#### name - -Name of investigation object - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Get DNS Server History for a host -``` - - - - -#### type - -Type of investigation object - -`type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#investigations-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### type Example - -```json -splunk -``` - - - - - - - - - -## known_false_positives - -Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior - -`known_false_positives` - -* is **required** -* type: `string` -* defined in this schema - -### known_false_positives Type - - -`string` - - - - - - -### known_false_positives Example - -```json -"Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate." -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## mappings - -Mappings to various industry standards and frameworks - -`mappings` - -* is optional -* type: `object` -* defined in this schema - -### mappings Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cis20`| array | Optional | -| `emoji`| array | Optional | -| `kill_chain_phases`| array | Optional | -| `mitre_attack`| array | Optional | -| `nist`| array | Optional | - - - -#### cis20 - -A list of critical security controls this search helps you implement - -`cis20` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### cis20 Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of critical security controls this search helps you implement", - "examples": [ - "CIS 12" - ], - "items": { - "enum": [ - "CIS 1", - "CIS 2", - "CIS 3", - "CIS 4", - "CIS 5", - "CIS 6", - "CIS 7", - "CIS 8", - "CIS 9", - "CIS 10", - "CIS 11", - "CIS 12", - "CIS 13", - "CIS 14", - "CIS 15", - "CIS 16", - "CIS 17", - "CIS 18", - "CIS 19", - "CIS 20" - ], - "simpletype": "`enum`", - "meta:enum": { - "CIS 1": "", - "CIS 2": "", - "CIS 3": "", - "CIS 4": "", - "CIS 5": "", - "CIS 6": "", - "CIS 7": "", - "CIS 8": "", - "CIS 9": "", - "CIS 10": "", - "CIS 11": "", - "CIS 12": "", - "CIS 13": "", - "CIS 14": "", - "CIS 15": "", - "CIS 16": "", - "CIS 17": "", - "CIS 18": "", - "CIS 19": "", - "CIS 20": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### cis20 Example - -```json -CIS 12 -``` - - - - -#### emoji - -A list of security emojis that will help UBA understand this alert as an external alarm - -`emoji` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### emoji Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of security emojis that will help UBA understand this alert as an external alarm", - "examples": [ - "EndPoint" - ], - "items": { - "enum": [ - "EndPoint", - "AD", - "Firewall", - "ApplicationLog", - "IPS", - "CloudData", - "Correlation", - "Printer", - "Badge" - ], - "simpletype": "`enum`", - "meta:enum": { - "EndPoint": "", - "AD": "", - "Firewall": "", - "ApplicationLog": "", - "IPS": "", - "CloudData": "", - "Correlation": "", - "Printer": "", - "Badge": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### emoji Example - -```json -EndPoint -``` - - - - -#### kill_chain_phases - -A list of kill-chain phases to which the search applies - -`kill_chain_phases` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### kill_chain_phases Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of kill-chain phases to which the search applies", - "examples": [ - "Reconnaissance" - ], - "items": { - "enum": [ - "Reconnaissance", - "Weaponization", - "Delivery", - "Exploitation", - "Installation", - "Command and Control", - "Actions on Objectives" - ], - "simpletype": "`enum`", - "meta:enum": { - "Reconnaissance": "", - "Weaponization": "", - "Delivery": "", - "Exploitation": "", - "Installation": "", - "Command and Control": "", - "Actions on Objectives": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### kill_chain_phases Example - -```json -Reconnaissance -``` - - - - -#### mitre_attack - -A list of the techniques and tactics identified by the search - -`mitre_attack` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### mitre_attack Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of the techniques and tactics identified by the search", - "examples": [ - "Defense Evasion", - "Initial Access" - ], - "items": { - "enum": [ - "Initial Access", - "Execution", - "Persistence", - "Spearphishing Attachment", - "Spearphishing Link", - "Privilege Escalation", - "Defense Evasion", - "Credential Access", - "Discovery", - "Lateral Movement", - "Collection", - "Exfiltration", - "Command and Control", - "Command and Control Protocol", - "Commonly Used Port", - "Custom Cryptographic Protocol", - "DLL Injection", - "DLL Search Order Hijacking", - "DLL Side-Loading", - "Data Compressed", - "Data Encrypted", - "Data Obfuscation", - "Data Staged", - "Data Transfer Size Limits", - "Data from Local System", - "Data from Network Shared Drive", - "Data from Removable Media", - "Disabling Security Tools", - "Email Collection", - "Execution through API", - "Exfiltration Over Alternative Protocol", - "Exfiltration Over Command and Control Channel", - "Exfiltration Over Other Network Medium", - "Exfiltration Over Physical Medium", - "Exploitation of Vulnerability", - "Fallback Channels", - "File Deletion", - "File System Logical Offsets", - "File System Permissions Weakness", - "File and Directory Discovery", - "Graphical User Interface", - "Hypervisor", - "Indicator Blocking", - "Indicator Removal from Tools", - "Indicator Removal on Host", - "Input Capture", - "InstallUtil", - "Legitimate Credentials", - "Local Network Configuration Discovery", - "Local Network Connections Discovery", - "Local Port Monitor", - "Logon Scripts", - "MSBuild", - "Masquerading", - "Modify Existing Service", - "Modify Registry", - "Multi-Stage Channels", - "Multiband Communication", - "Multilayer Encryption", - "NTFS Extended Attributes", - "Network Service Scanning", - "Network Share Connection Removal", - "Network Sniffing", - "New Service", - "Obfuscated Files or Information", - "Pass the Hash", - "Pass the Ticket", - "Path Interception", - "Peripheral Device Discovery", - "Permission Groups Discovery", - "PowerShell", - "Process Discovery", - "Process Hollowing", - "Query Registry", - "Redundant Access", - "Registry Run Keys / Start Folder", - "Regsvcs/Regasm", - "Regsvr32", - "Remote Desktop Protocol", - "Create Account", - "Remote File Copy", - "Remote Services", - "Remote System Discovery", - "Replication Through Removable Media", - "Rootkit", - "Rundll32", - "Scheduled Task", - "Scheduled Transfer", - "Screen Capture", - "Scripting", - "Security Software Discovery", - "Security Support Provider", - "Service Execution", - "Service Registry Permissions Weakness", - "Shared Webroot", - "Shortcut Modification", - "Software Packing", - "Standard Application Layer Protocol", - "Standard Cryptographic Protocol", - "Standard Non-Application Layer Protocol", - "System Information Discovery", - "System Owner/User Discovery", - "System Service Discovery", - "System Time Discovery", - "Taint Shared Content", - "Third-party Software", - "Timestomp", - "Two-Factor Authentication Interception", - "Uncommonly Used Port", - "Video Capture", - "Valid Accounts", - "Web Service", - "Web Shell", - "Windows Admin Shares", - "Windows Management Instrumentation Event Subscription", - "Windows Management Instrumentation", - "Windows Remote Management", - "Winlogon Helper DLL", - "Exploitation for Privilege Escalation" - ], - "simpletype": "`enum`", - "meta:enum": { - "Initial Access": "", - "Execution": "", - "Persistence": "", - "Spearphishing Attachment": "", - "Spearphishing Link": "", - "Privilege Escalation": "", - "Defense Evasion": "", - "Credential Access": "", - "Discovery": "", - "Lateral Movement": "", - "Collection": "", - "Exfiltration": "", - "Command and Control": "", - "Command and Control Protocol": "", - "Commonly Used Port": "", - "Custom Cryptographic Protocol": "", - "DLL Injection": "", - "DLL Search Order Hijacking": "", - "DLL Side-Loading": "", - "Data Compressed": "", - "Data Encrypted": "", - "Data Obfuscation": "", - "Data Staged": "", - "Data Transfer Size Limits": "", - "Data from Local System": "", - "Data from Network Shared Drive": "", - "Data from Removable Media": "", - "Disabling Security Tools": "", - "Email Collection": "", - "Execution through API": "", - "Exfiltration Over Alternative Protocol": "", - "Exfiltration Over Command and Control Channel": "", - "Exfiltration Over Other Network Medium": "", - "Exfiltration Over Physical Medium": "", - "Exploitation of Vulnerability": "", - "Fallback Channels": "", - "File Deletion": "", - "File System Logical Offsets": "", - "File System Permissions Weakness": "", - "File and Directory Discovery": "", - "Graphical User Interface": "", - "Hypervisor": "", - "Indicator Blocking": "", - "Indicator Removal from Tools": "", - "Indicator Removal on Host": "", - "Input Capture": "", - "InstallUtil": "", - "Legitimate Credentials": "", - "Local Network Configuration Discovery": "", - "Local Network Connections Discovery": "", - "Local Port Monitor": "", - "Logon Scripts": "", - "MSBuild": "", - "Masquerading": "", - "Modify Existing Service": "", - "Modify Registry": "", - "Multi-Stage Channels": "", - "Multiband Communication": "", - "Multilayer Encryption": "", - "NTFS Extended Attributes": "", - "Network Service Scanning": "", - "Network Share Connection Removal": "", - "Network Sniffing": "", - "New Service": "", - "Obfuscated Files or Information": "", - "Pass the Hash": "", - "Pass the Ticket": "", - "Path Interception": "", - "Peripheral Device Discovery": "", - "Permission Groups Discovery": "", - "PowerShell": "", - "Process Discovery": "", - "Process Hollowing": "", - "Query Registry": "", - "Redundant Access": "", - "Registry Run Keys / Start Folder": "", - "Regsvcs/Regasm": "", - "Regsvr32": "", - "Remote Desktop Protocol": "", - "Create Account": "", - "Remote File Copy": "", - "Remote Services": "", - "Remote System Discovery": "", - "Replication Through Removable Media": "", - "Rootkit": "", - "Rundll32": "", - "Scheduled Task": "", - "Scheduled Transfer": "", - "Screen Capture": "", - "Scripting": "", - "Security Software Discovery": "", - "Security Support Provider": "", - "Service Execution": "", - "Service Registry Permissions Weakness": "", - "Shared Webroot": "", - "Shortcut Modification": "", - "Software Packing": "", - "Standard Application Layer Protocol": "", - "Standard Cryptographic Protocol": "", - "Standard Non-Application Layer Protocol": "", - "System Information Discovery": "", - "System Owner/User Discovery": "", - "System Service Discovery": "", - "System Time Discovery": "", - "Taint Shared Content": "", - "Third-party Software": "", - "Timestomp": "", - "Two-Factor Authentication Interception": "", - "Uncommonly Used Port": "", - "Video Capture": "", - "Valid Accounts": "", - "Web Service": "", - "Web Shell": "", - "Windows Admin Shares": "", - "Windows Management Instrumentation Event Subscription": "", - "Windows Management Instrumentation": "", - "Windows Remote Management": "", - "Winlogon Helper DLL": "", - "Exploitation for Privilege Escalation": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### mitre_attack Examples - -```json -Defense Evasion -``` - -```json -Initial Access -``` - - - - - -#### nist - -A list of the NIST controls the search helps you implement - -`nist` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### nist Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of the NIST controls the search helps you implement", - "examples": [ - "ID.AM", - "PR.PT" - ], - "items": { - "enum": [ - "ID.AM", - "ID.RA", - "PR.DS", - "PR.IP", - "PR.AC", - "PR.PT", - "PR.AT", - "PR.MA", - "DE.CM", - "DE.DP", - "DE.AE", - "RS.MI", - "RS.AN", - "RS.RP", - "RS.IM", - "RS.CO", - "RC.IM", - "RC.CO" - ], - "simpletype": "`enum`", - "meta:enum": { - "ID.AM": "", - "ID.RA": "", - "PR.DS": "", - "PR.IP": "", - "PR.AC": "", - "PR.PT": "", - "PR.AT": "", - "PR.MA": "", - "DE.CM": "", - "DE.DP": "", - "DE.AE": "", - "RS.MI": "", - "RS.AN": "", - "RS.RP": "", - "RS.IM": "", - "RS.CO": "", - "RC.IM": "", - "RC.CO": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### nist Examples - -```json -ID.AM -``` - -```json -PR.PT -``` - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2019-02-14" -``` - - -## name - -The name of the detection - -`name` - -* is optional -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - -### name Example - -```json -"DNS record changed" -``` - - -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## references - -A list of URLs that give more information about the search - -`references` - -* is optional -* type: `string[]` -* at least `0` items in the array -* defined in this schema - -### references Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -### references Example - -```json -"https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" -``` - - -## responses - -An array of the response objects to exectute on the detection results - -`responses` - -* is optional -* type: `object[]` -* defined in this schema - -### responses Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `type`| string | **Required** | - - - -#### id - -UUID of the Respose object - -`id` - -* is **required** -* type: `string` - -##### id Type - - -`string` - - - - - - -##### id Example - -```json -1169w17b-ef78-4b59-aae8-5369073014e1 -``` - - - - -#### name - -Name of Response Object - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -DNS Hijack Reponse -``` - - - - -#### type - -Type of baseline to execute - -`type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#responses-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### type Example - -```json -phantom -``` - - - - - - - - - -## security_domain - -The high-level security area to which the search belongs - -`security_domain` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#security_domain-known-values). - -### security_domain Known Values -| Value | Description | -|-------|-------------| -| `access` | | -| `endpoint` | | -| `network` | | -| `threat` | | - - - -### security_domain Example - -```json -"endpoint" -``` - - -## spec_version - -The version of the detection specification this manifest follows - -`spec_version` - -* is optional -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -"2.0" -``` - - -## type - -The type of detection - -`type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#type-known-values). - -### type Known Values -| Value | Description | -|-------|-------------| -| `uba` | | -| `splunk` | | -| `phantom` | | - - - -### type Example - -```json -"phantom" -``` - - -## version - -The version of the detection - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - -### version Example - -```json -"1" -``` - - -# Detection Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [correlation_rule](#correlation_rule) | `object` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [event_type](#event_type) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [model](#model) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [model_version](#model_version) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [threat_category](#threat_category) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | - -## correlation_rule - -Various fields to enhance usability in Enterprise Security - -`correlation_rule` - -* is optional -* type: `object` -* defined in this schema - -### correlation_rule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `notable`| object | Optional | -| `risk`| object | Optional | -| `schedule`| object | **Required** | -| `search`| string | **Required** | -| `suppress`| object | Optional | - - - -#### notable - -Various fields associated with creating a notable event - -`notable` - -* is optional -* type: `object` - -##### notable Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `nes_fields`| string | **Required** | -| `rule_description`| string | **Required** | -| `rule_title`| string | **Required** | - - - -#### nes_fields - -A list of suggested fields to be used for notable-event suppression - -`nes_fields` - -* is **required** -* type: `string` - -##### nes_fields Type - - -`string` - - - - - - - - - -#### rule_description - -Description of the notable event that will display in Incident Review - -`rule_description` - -* is **required** -* type: `string` - -##### rule_description Type - - -`string` - - - - - - - - - -#### rule_title - -Title of the notable event that will display in Incident Review - -`rule_title` - -* is **required** -* type: `string` - -##### rule_title Type - - -`string` - - - - - - - - - - - - - - -#### risk - -Fields associated with assigning risk to objects - -`risk` - -* is optional -* type: `object` - -##### risk Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `risk_object`| string | **Required** | -| `risk_object_type`| array | **Required** | -| `risk_score`| integer | **Required** | - - - -#### risk_object - -TThe field to which you are assigning risk - -`risk_object` - -* is **required** -* type: `string` - -##### risk_object Type - - -`string` - - - - - - - - - -#### risk_object_type - -The type of object to which you are assigning risk - -`risk_object_type` - -* is **required** -* type: `enum[]`* between `0` and `1` items in the array - - -##### risk_object_type Type - - -Array type: `enum[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### risk_score - -Score assigned to risk_object - -`risk_score` - -* is **required** -* type: `integer` - -##### risk_score Type - - -`integer` - - - - - - - - - - - - - - -#### schedule - -Various fields to assist in scheduling the search - -`schedule` - -* is **required** -* type: `object` - -##### schedule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cron_schedule`| string | Optional | -| `earliest_time`| string | Optional | -| `latest_time`| string | Optional | - - - -#### cron_schedule - -Schedule of the search in cron format - -`cron_schedule` - -* is optional -* type: `string` - -##### cron_schedule Type - - -`string` - - - - - - - - - -#### earliest_time - -The earliest time the search should run in Splunk format - -`earliest_time` - -* is optional -* type: `string` - -##### earliest_time Type - - -`string` - - - - - - - - - -#### latest_time - -The latest time tes search should run against in Splunk format - -`latest_time` - -* is optional -* type: `string` - -##### latest_time Type - - -`string` - - - - - - - - - - - - - - -#### search - -The search (in SPL) executed within core Splunk - -`search` - -* is **required** -* type: `string` - -##### search Type - - -`string` - - - - - - - - - -#### suppress - -Fields associated with suppressing the creation of multiple alerts - -`suppress` - -* is optional -* type: `object` - -##### suppress Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `suppress_fields`| string | **Required** | -| `suppress_period`| string | **Required** | - - - -#### suppress_fields - -The fields to base the suppression on - -`suppress_fields` - -* is **required** -* type: `string` - -##### suppress_fields Type - - -`string` - - - - - - - - - -#### suppress_period - -The length of time the suppression should be in effect - -`suppress_period` - -* is **required** -* type: `string` - -##### suppress_period Type - - -`string` - - - - - - - - - - - - - - - - - -## event_type - -An anomaly or threat. - -`event_type` - -* is optional -* type: `string` -* defined in this schema - -### event_type Type - - -`string` - - - - - - -### event_type Example - -```json -"anomaly" -``` - - -## model - -The name of the Splunk UBA model that detected the anomaly. - -`model` - -* is optional -* type: `string` -* defined in this schema - -### model Type - - -`string` - - - - - - -### model Example - -```json -"UBA Model" -``` - - -## model_version - -Url of the playbook on Phantom website. - -`model_version` - -* is optional -* type: `string` -* defined in this schema - -### model_version Type - - -`string` - - - - - - -### model_version Example - -```json -"2.0" -``` - - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - -### phantom_server Example - -```json -"automation (hostname)" -``` - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - -### playbook_name Example - -```json -"community/dns_hijack_detect_playbook" -``` - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - -### playbook_url Example - -```json -"https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" -``` - - -## sensitivity - -TLP colors (white, green, amber or red) - -`sensitivity` - -* is optional -* type: `string` -* defined in this schema - -### sensitivity Type - - -`string` - - - - - - -### sensitivity Example - -```json -"green" -``` - - -## severity - -Severity in phantom (High, Medium, Low) - -`severity` - -* is optional -* type: `string` -* defined in this schema - -### severity Type - - -`string` - - - - - - -### severity Example - -```json -"high" -``` - - -## threat_category - -The category of a threat in Splunk UBA. - -`threat_category` - -* is optional -* type: `string` -* defined in this schema - -### threat_category Type - - -`string` - - - - - - -### threat_category Example - -```json -"Malware" -``` - diff --git a/docs/investigations.spec.json b/docs/investigations.spec.json deleted file mode 100644 index c597b8b8a1..0000000000 --- a/docs/investigations.spec.json +++ /dev/null @@ -1,372 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/investigations.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the Phantom server. Currently, we ship these values as automation (hostname) and encourage users to modify them according to their environments: for example, automation (hostname)", - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on the Phantom community repository on github, with underscores, and appended with \"community/.\" The playbooks are hosted on https://github.com/phantomcyber/playbooks. For example: community/simple_network_enrichment.", - "type": "string" - }, - "playbook_url": { - "description": "URL of the playbook on the Phantom website", - "type": "string" - }, - "schedule": { - "description": "Various fields used to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber, or Red)", - "type": "string" - }, - "severity": { - "description": "Severity in Phantom (High, Medium, Low)", - "type": "string" - } - }, - "required": [ - "playbook_name", - "phantom_server", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "properties": { - "fields_required": { - "description": "A list of data models, if any, used by this search", - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run again in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within Splunk Enterprise for investigation", - "type": "string" - } - }, - "required": [ - "search", - "fields_required", - "schedule" - ], - "type": "object" - } - }, - "description": "The fields that make up the manifest of a version 2 investigative object", - "properties": { - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "description": "Information about the data being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "OSquery", - "SMTP", - "Cuckoo", - "VirusTotal", - "DeepSight" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "eli5": { - "description": "Explain it like I\u2019m 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "type": "string" - }, - "entities": { - "description": "A list of entities that will used in the story flow or are relevant to the security investigation. ", - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion of how to implement this search, from what needs to be ingested, config files modified, and suggested per-site modifications", - "type": "string" - }, - "id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "investigate": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - } - }, - "type": "object" - }, - "known_false_positives": { - "description": "Scenarios in which detected behavior is benign, coupled with suggestions on how to verify the behavior", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "name": { - "description": "The name of the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "spec_version": { - "description": "The version of the investigative search specification this manifest follows", - "type": "integer" - }, - "type": { - "description": "Type of product that will support this investigate object", - "enum": [ - "phantom", - "splunk", - "uba" - ] - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "required": [ - "creation_date", - "data_metadata", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "description", - "id", - "type", - "spec_version", - "version", - "investigate" - ], - "title": "Investigative Search Manifest", - "type": "object" -} diff --git a/docs/investigations.spec.md b/docs/investigations.spec.md deleted file mode 100644 index 9a3a5513d7..0000000000 --- a/docs/investigations.spec.md +++ /dev/null @@ -1,1165 +0,0 @@ - -# Investigative Search Manifest Schema - -``` -https://api.splunkresearch.com/schemas/investigations.json -``` - -The fields that make up the manifest of a version 2 investigative object - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Permitted | | - -# Investigative Search Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [creation_date](#creation_date) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Investigative Search Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [eli5](#eli5) | `string` | Optional | No | Investigative Search Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Investigative Search Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [investigate](#investigate) | `object` | **Required** | No | Investigative Search Manifest (this schema) | -| [known_false_positives](#known_false_positives) | `string` | Optional | No | Investigative Search Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Investigative Search Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [name](#name) | `string` | Optional | No | Investigative Search Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Investigative Search Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | **Required** | No | Investigative Search Manifest (this schema) | -| [type](#type) | `enum` | **Required** | No | Investigative Search Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## creation_date - -The date the story manifest was created - -`creation_date` - -* is **required** -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - - -## data_metadata - -Information about the date being ingested - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Change": "", - "Cloud_Infrastructure": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "OSquery", - "SMTP", - "Cuckoo", - "VirusTotal", - "DeepSight" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "", - "Censys": "", - "OSquery": "", - "SMTP": "", - "Cuckoo": "", - "VirusTotal": "", - "DeepSight": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - - - - - - -## description - -A description of what the search is designed to detect - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - - -## eli5 - -Explain it like I’m 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert - -`eli5` - -* is optional -* type: `string` -* defined in this schema - -### eli5 Type - - -`string` - - - - - - - -## entities - -A list of entities that will used in the story flow or are relevant to the security investigation. - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that will used in the story flow or are relevant to the security investigation. ", - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - -## how_to_implement - -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications - -`how_to_implement` - -* is **required** -* type: `string` -* defined in this schema - -### how_to_implement Type - - -`string` - - - - - - - -## id - -The unique identifier for the search - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - - -## investigate - - -`investigate` - -* is **required** -* type: `object` -* defined in this schema - -### investigate Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `phantom`| | Optional | -| `splunk`| | Optional | - - - -#### phantom - - -`phantom` - -* is optional -* type: reference - -##### phantom Type - - -* []() – `#/definitions/phantom` - - - - - - - -#### splunk - - -`splunk` - -* is optional -* type: reference - -##### splunk Type - - -* []() – `#/definitions/splunk` - - - - - - - - - - -## known_false_positives - -Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior - -`known_false_positives` - -* is optional -* type: `string` -* defined in this schema - -### known_false_positives Type - - -`string` - - - - - - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - - -## name - -The name of the search - -`name` - -* is optional -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - - -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## spec_version - -The version of the investigative search specification this manifest follows - -`spec_version` - -* is **required** -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - - -## type - -Type of product that will support this investigate object. - -`type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#type-known-values). - -### type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - - -## version - -The version of the search - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - - -# Investigative Search Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [fields_required](#fields_required) | `array` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/splunk` | -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [schedule](#schedule) | `object` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/splunk` | -| [search](#search) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/splunk` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | - -## fields_required - -A list of data models, if any, used by this search - -`fields_required` - -* is optional -* type: `array` -* at least `0` items in the array -* defined in this schema - -### fields_required Type - - -Array type: `array` - - - - - - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - - -## schedule - -Various fields to assist in scheduling the search - -`schedule` - -* is optional -* type: `object` -* defined in this schema - -### schedule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cron_schedule`| string | Optional | -| `earliest_time`| string | Optional | -| `latest_time`| string | Optional | - - - -#### cron_schedule - -Schedule of the search in cron format - -`cron_schedule` - -* is optional -* type: `string` - -##### cron_schedule Type - - -`string` - - - - - - - - - -#### earliest_time - -The earliest time the search should run in Splunk format - -`earliest_time` - -* is optional -* type: `string` - -##### earliest_time Type - - -`string` - - - - - - - - - -#### latest_time - -The latest time tes search should run against in Splunk format - -`latest_time` - -* is optional -* type: `string` - -##### latest_time Type - - -`string` - - - - - - - - - - - - -## search - -The search (in SPL) executed within core Splunk for investgation. - -`search` - -* is optional -* type: `string` -* defined in this schema - -### search Type - - -`string` - - - - - - - -## sensitivity - -TLP colors (White, Green, Amber or Red) - -`sensitivity` - -* is optional -* type: `string` -* defined in this schema - -### sensitivity Type - - -`string` - - - - - - - -## severity - -Severity in phantom (High, Medium, Low) - -`severity` - -* is optional -* type: `string` -* defined in this schema - -### severity Type - - -`string` - - - - - - diff --git a/docs/spec/baselines.spec.md b/docs/spec/baselines.spec.md index 8d76a5c212..fc66502080 100644 --- a/docs/spec/baselines.spec.md +++ b/docs/spec/baselines.spec.md @@ -1,112 +1,43 @@ -# Baseline Manifest Schema +# Baseline Schema Schema ``` -https://api.splunkresearch.com/schemas/baselines.json +http://example.com/example.json ``` -The fields that make up the manifest of a version 2 baseline search +schema for baselines | Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | |----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Permitted | | +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | -# Baseline Manifest Properties +# Baseline Schema Properties -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [baseline](#baseline) | `object` | **Required** | No | Baseline Manifest (this schema) | -| [creation_date](#creation_date) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Baseline Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [eli5](#eli5) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Baseline Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [known_false_positives](#known_false_positives) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Baseline Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [name](#name) | `string` | **Required** | No | Baseline Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Baseline Manifest (this schema) | -| [product_type](#product_type) | `enum` | **Required** | No | Baseline Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | **Required** | No | Baseline Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Baseline Manifest (this schema) | +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [author](#author) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [how_to_implement](#how_to_implement) | `string` | Optional | No | `""` | Baseline Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [search](#search) | `string` | **Required** | No | `""` | Baseline Schema (this schema) | +| [tags](#tags) | `object` | **Required** | No | `{}` | Baseline Schema (this schema) | +| [version](#version) | `integer` | **Required** | No | `0` | Baseline Schema (this schema) | | `*` | any | Additional | Yes | this schema *allows* additional properties | -## baseline +## author +Author of the baseline -`baseline` - -* is **required** -* type: `object` -* defined in this schema - -### baseline Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `phantom`| | Optional | -| `splunk`| | Optional | - - - -#### phantom - - -`phantom` - -* is optional -* type: reference - -##### phantom Type - - -* []() – `#/definitions/phantom` - - - - - - - -#### splunk - - -`splunk` - -* is optional -* type: reference - -##### splunk Type - - -* []() – `#/definitions/splunk` - - - - - - - - - - -## creation_date - -The date the baseline manifest was created - -`creation_date` +`author` * is **required** * type: `string` +* default: `""` * defined in this schema -### creation_date Type +### author Type `string` @@ -116,55 +47,27 @@ The date the baseline manifest was created -### creation_date Example +### author Example ```json -"2019-02-14" +"Bahvin Patel, Splunk" ``` -## data_metadata +## date -Information about the date being ingested +date of creation or modification, format yyyy-mm-dd -`data_metadata` +`date` * is **required** -* type: `object` +* type: `string` +* default: `""` * defined in this schema -### data_metadata Type +### date Type -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: `string` @@ -172,278 +75,22 @@ All items must be of the type: - - -##### data_eventtypes Example +### date Example ```json -wineventlog +"2019-12-06" ``` - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "type": "array", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Change": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "minItems": 0, - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### data_models Example - -```json -Network_Resolution -``` - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_source Example - -```json -DNS -``` - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_sourcetypes Example - -```json -stream:dns -``` - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### providing_technologies Example - -```json -Bro -``` - - - - - - - ## description -A description of what the search is is doing to create a baseline +A detailed description of the baseline `description` * is **required** * type: `string` +* default: `""` * defined in this schema ### description Type @@ -459,148 +106,19 @@ A description of what the search is is doing to create a baseline ### description Example ```json -"The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Traffic` datamodel, then stores the output under the `discovered_dns_records.csv` lookup" +"This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days" ``` -## eli5 - -Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert - -`eli5` - -* is **required** -* type: `string` -* defined in this schema - -### eli5 Type - - -`string` - - - - - - -### eli5 Example - -```json -"Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`" -``` - - -## entities - -A list of entities that will used in the story flow or are relevant to the security investigation. - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that will used in the story flow or are relevant to the security investigation.", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -### entities Examples - -```json -"dest" -``` - -```json -"user" -``` - - - ## how_to_implement -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications +information about how to implement. Only needed for non standard implementations. `how_to_implement` -* is **required** +* is optional * type: `string` +* default: `""` * defined in this schema ### how_to_implement Type @@ -616,18 +134,19 @@ A discussion on how to implement this search, from what needs to be ingested, co ### how_to_implement Example ```json -"To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation" +"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." ``` ## id -The unique identifier for the search +UUID as unique identifier `id` * is **required** * type: `string` +* default: `""` * defined in this schema ### id Type @@ -643,188 +162,18 @@ The unique identifier for the search ### id Example ```json -"c096f721-8842-42ce-bfc7-74bd8c72b7c3" -``` - - -## known_false_positives - -Describe the known false postives while the analyst builds the baseline. - -`known_false_positives` - -* is **required** -* type: `string` -* defined in this schema - -### known_false_positives Type - - -`string` - - - - - - -### known_false_positives Example - -```json -"Please vet the lookup created by this baseline search." -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2019-02-14" +"fc0edc95-ff2b-48b0-9f6f-63da3789fd63" ``` ## name - -The name of the search that creates the baseline +### Name of baseline `name` * is **required** * type: `string` +* default: `""` * defined in this schema ### name Type @@ -840,425 +189,19 @@ The name of the search that creates the baseline ### name Example ```json -"Discover DNS records" +"Previously Seen AWS Regions" ``` -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## product_type - -The type of baseline - -`product_type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#product_type-known-values). - -### product_type Known Values -| Value | Description | -|-------|-------------| -| `splunk` | | -| `phantom` | | -| `uba` | | - - - -### product_type Example - -```json -"splunk" -``` - - -## spec_version - -The version of the detection search specification this manifest follows - -`spec_version` - -* is **required** -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -"2.0" -``` - - -## version - -The version of the search - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - -### version Examples - -```json -"1" -``` - -```json -"2" -``` - - - -# Baseline Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [schedule](#schedule) | `object` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/splunk` | -| [search](#search) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/splunk` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/baselines.json#/definitions/phantom` | - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - -### phantom_server Example - -```json -"automation (hostname)" -``` - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - -### playbook_name Example - -```json -"community/dns_hijack_investigation" -``` - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - -### playbook_url Example - -```json -"https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" -``` - - -## schedule - -Various fields to assist in scheduling the search - -`schedule` - -* is optional -* type: `object` -* defined in this schema - -### schedule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cron_schedule`| string | Optional | -| `earliest_time`| string | Optional | -| `latest_time`| string | Optional | - - - -#### cron_schedule - -Schedule of the search in cron format - -`cron_schedule` - -* is optional -* type: `string` - -##### cron_schedule Type - - -`string` - - - - - - -##### cron_schedule Example - -```json -0 * * * * -``` - - - - -#### earliest_time - -The earliest time the search should run in Splunk format - -`earliest_time` - -* is optional -* type: `string` - -##### earliest_time Type - - -`string` - - - - - - -##### earliest_time Example - -```json --70m@m -``` - - - - -#### latest_time - -The latest time tes search should run against in Splunk format - -`latest_time` - -* is optional -* type: `string` - -##### latest_time Type - - -`string` - - - - - - -##### latest_time Example - -```json --10m@m -``` - - - - - - - ## search -The search (in SPL) executed within core Splunk for investgation. +The Splunk search for the baseline `search` -* is optional +* is **required** * type: `string` +* default: `""` * defined in this schema ### search Type @@ -1274,60 +217,67 @@ The search (in SPL) executed within core Splunk for investgation. ### search Example ```json -"| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)" +"cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv" ``` -## sensitivity +## tags -TLP colors (White, Green, Amber or Red) +An array of key value pairs for tagging -`sensitivity` +`tags` -* is optional -* type: `string` +* is **required** +* type: `object` +* default: `{}` * defined in this schema -### sensitivity Type +### tags Type -`string` +`object` with following properties: + + +| Property | Type | Required | +|----------|------|----------| - - -### sensitivity Example +### tags Example ```json -"green" +{ + "analytics_story": "suspicious_aws_ec2_activities", + "custom_key": "custom_value" +} ``` -## severity +## version -Severity in phantom (High, Medium, Low) +version of baseline, e.g. 1 or 2 ... -`severity` +`version` -* is optional -* type: `string` +* is **required** +* type: `integer` +* default: `0` * defined in this schema -### severity Type +### version Type -`string` +`integer` -### severity Example +### version Example ```json -"medium" +1 ``` diff --git a/docs/spec/deployments.spec.md b/docs/spec/deployments.spec.md new file mode 100644 index 0000000000..8d86ed47ff --- /dev/null +++ b/docs/spec/deployments.spec.md @@ -0,0 +1,656 @@ + +# Deployment Schema Schema + +``` +http://example.com/example.json +``` + +schema for deployment + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | +|----------|------------|--------|--------------|-------------------|-----------------------|------------| +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | + +# Deployment Schema Properties + +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [alert_action](#alert_action) | `object` | **Required** | No | `{}` | Deployment Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Deployment Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Deployment Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Deployment Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Deployment Schema (this schema) | +| [scheduling](#scheduling) | `object` | **Required** | No | `{}` | Deployment Schema (this schema) | +| [tags](#tags) | `object` | **Required** | No | `{}` | Deployment Schema (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | + +## alert_action + +Set alert action parameter for search + +`alert_action` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### alert_action Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `email`| object | Optional | `{}` | +| `index`| object | Optional | `{}` | +| `notable`| object | Optional | `{}` | + + + +#### email + +By enabling it, an email is sent with the results + +`email` + +* is optional +* type: `object` +* default: `{}` + + +##### email Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `message`| string | **Required** | `""` | +| `subject`| string | **Required** | `""` | +| `to`| string | **Required** | `""` | + + + +#### message + +message of email + +`message` + +* is **required** +* type: `string` +* default: `""` + + +##### message Type + + +`string` + + + + + + +##### message Example + +```json +Splunk Alert $name$ triggered %fields% +``` + + + + +#### subject + +Subject of email + +`subject` + +* is **required** +* type: `string` +* default: `""` + + +##### subject Type + + +`string` + + + + + + +##### subject Example + +```json +Splunk Alert $name$ +``` + + + + +#### to + +Recipient of email + +`to` + +* is **required** +* type: `string` +* default: `""` + + +##### to Type + + +`string` + + + + + + +##### to Example + +```json +test@test.com +``` + + + + + +##### email Example + +```json +[object Object] +``` + + + + +#### index + +By enabling it, the results are stored in another index + +`index` + +* is optional +* type: `object` +* default: `{}` + + +##### index Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `name`| string | **Required** | `""` | + + + +#### name + +Name of the index + +`name` + +* is **required** +* type: `string` +* default: `""` + + +##### name Type + + +`string` + + + + + + +##### name Example + +```json +asx +``` + + + + + +##### index Example + +```json +[object Object] +``` + + + + +#### notable + +By enabling it, a notable is generated + +`notable` + +* is optional +* type: `object` +* default: `{}` + + +##### notable Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `rule_description`| string | **Required** | `""` | +| `rule_title`| string | **Required** | `""` | + + + +#### rule_description + +Rule description of the notable event + +`rule_description` + +* is **required** +* type: `string` +* default: `""` + + +##### rule_description Type + + +`string` + + + + + + +##### rule_description Example + +```json +%description% +``` + + + + +#### rule_title + +Rule title of the notable event + +`rule_title` + +* is **required** +* type: `string` +* default: `""` + + +##### rule_title Type + + +`string` + + + + + + +##### rule_title Example + +```json +%name% +``` + + + + + +##### notable Example + +```json +[object Object] +``` + + + + + +### alert_action Example + +```json +{ + "email": { + "message": "Splunk Alert $name$ triggered %fields%", + "subject": "Splunk Alert $name$", + "to": "test@test.com" + }, + "index": { + "name": "asx" + }, + "notable": { + "rule_description": "%description%", + "rule_title": "%name%" + } +} +``` + + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### date Type + + +`string` + + + + + + +### date Example + +```json +"2019-12-06" +``` + + +## description + +description of the deployment configuration + +`description` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### description Type + + +`string` + + + + + + +### description Example + +```json +"This deployment configuration provides a standard scheduling policy over all rules." +``` + + +## id + +uuid as unique identifier + +`id` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### id Type + + +`string` + + + + + + +### id Example + +```json +"fb4c31b0-13e8-4155-8aa5-24de4b8d6717" +``` + + +## name + +Name of deployment configuration + +`name` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### name Type + + +`string` + + + + + + +### name Example + +```json +"Deployment Configuration all Detections" +``` + + +## scheduling + +allows to set scheduling parameter + +`scheduling` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### scheduling Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `cron_schedule`| string | **Required** | `""` | +| `earliest_time`| string | **Required** | `""` | +| `latest_time`| string | **Required** | `""` | +| `schedule_window`| string | Optional | `""` | + + + +#### cron_schedule + +Cron schedule to schedule the Splunk searches. + +`cron_schedule` + +* is **required** +* type: `string` +* default: `""` + + +##### cron_schedule Type + + +`string` + + + + + + +##### cron_schedule Example + +```json +*/10 * * * * +``` + + + + +#### earliest_time + +earliest time of search + +`earliest_time` + +* is **required** +* type: `string` +* default: `""` + + +##### earliest_time Type + + +`string` + + + + + + +##### earliest_time Example + +```json +-10m +``` + + + + +#### latest_time + +latest time of search + +`latest_time` + +* is **required** +* type: `string` +* default: `""` + + +##### latest_time Type + + +`string` + + + + + + +##### latest_time Example + +```json +now +``` + + + + +#### schedule_window + +schedule window for search + +`schedule_window` + +* is optional +* type: `string` +* default: `""` + + +##### schedule_window Type + + +`string` + + + + + + +##### schedule_window Example + +```json +auto +``` + + + + + +### scheduling Example + +```json +{ + "cron_schedule": "*/10 * * * *", + "earliest_time": "-10m", + "latest_time": "now", + "schedule_window": "auto" +} +``` + + +## tags + +An array of key value pairs for tagging + +`tags` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### tags Type + + +`object` with following properties: + + +| Property | Type | Required | +|----------|------|----------| + + + + +### tags Example + +```json +{ + "analytics_story": "credential_dumping" +} +``` + diff --git a/docs/spec/detections.spec.md b/docs/spec/detections.spec.md index d3ac1ec0ff..8fb01effff 100644 --- a/docs/spec/detections.spec.md +++ b/docs/spec/detections.spec.md @@ -1,110 +1,46 @@ -# Detection Manifest Schema +# Detection Schema Schema ``` -https://api.splunkresearch.com/schemas/detections.json +http://example.com/example.json ``` -A object that defines the parameters for detecting things using various Splunk capabilities +schema for detections | Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | |----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Permitted | | +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | -# Detection Manifest Properties +# Detection Schema Properties -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [asset_type](#asset_type) | `string` | Optional | No | Detection Manifest (this schema) | -| [baselines](#baselines) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [confidence](#confidence) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [creation_date](#creation_date) | `string` | **Required** | No | Detection Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Detection Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Detection Manifest (this schema) | -| [detect](#detect) | `object` | **Required** | No | Detection Manifest (this schema) | -| [eli5](#eli5) | `string` | **Required** | No | Detection Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Detection Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Detection Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Detection Manifest (this schema) | -| [investigations](#investigations) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [known_false_positives](#known_false_positives) | `string` | **Required** | No | Detection Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Detection Manifest (this schema) | -| [mappings](#mappings) | `object` | Optional | No | Detection Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Detection Manifest (this schema) | -| [name](#name) | `string` | Optional | No | Detection Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Detection Manifest (this schema) | -| [product_type](#product_type) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [references](#references) | `string[]` | Optional | No | Detection Manifest (this schema) | -| [responses](#responses) | `object[]` | Optional | No | Detection Manifest (this schema) | -| [security_domain](#security_domain) | `enum` | **Required** | No | Detection Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | Optional | No | Detection Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Detection Manifest (this schema) | +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [author](#author) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [how_to_implement](#how_to_implement) | `string` | Optional | No | `""` | Detection Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [known_false_positives](#known_false_positives) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [references](#references) | `string[]` | Optional | No | `[]` | Detection Schema (this schema) | +| [search](#search) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [tags](#tags) | `object` | **Required** | No | `{}` | Detection Schema (this schema) | +| [type](#type) | `string` | **Required** | No | `""` | Detection Schema (this schema) | +| [version](#version) | `integer` | **Required** | No | `0` | Detection Schema (this schema) | | `*` | any | Additional | Yes | this schema *allows* additional properties | -## asset_type +## author -Designates the type of asset being investigated +Author of the detection -`asset_type` - -* is optional -* type: `string` -* defined in this schema - -### asset_type Type - - -`string` - - - - - - -### asset_type Example - -```json -"Endpoint" -``` - - -## baselines - -An array of the baseline objects to exectute before the detection - -`baselines` - -* is optional -* type: `object[]` -* defined in this schema - -### baselines Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `type`| | Optional | - - - -#### id - -UUID of the baseline object - -`id` +`author` * is **required** * type: `string` +* default: `""` +* defined in this schema -##### id Type +### author Type `string` @@ -114,116 +50,25 @@ UUID of the baseline object -##### id Example +### author Example ```json -c096f721-8842-42ce-bfc7-74bd8c72b7c3 +"Patrick Bareiss, Splunk" ``` +## date +date of creation or modification, format yyyy-mm-dd -#### name - -name of baseline object - -`name` +`date` * is **required** * type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Discover DNS records -``` - - - - -#### type - -Type of baseline to execute - -`type` - -* is optional -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#baselines-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### type Example - -```json -splunk -``` - - - - - - - - - -## confidence - -Confidence that detected behavior is malicious - -`confidence` - -* is **required** -* type: `enum` +* default: `""` * defined in this schema -The value of this property **must** be equal to one of the [known values below](#confidence-known-values). - -### confidence Known Values -| Value | Description | -|-------|-------------| -| `high` | | -| `medium` | | -| `low` | | - - - -### confidence Example - -```json -"high" -``` - - -## creation_date - -The date the story manifest was created - -`creation_date` - -* is **required** -* type: `string` -* defined in this schema - -### creation_date Type +### date Type `string` @@ -233,336 +78,22 @@ The date the story manifest was created -### creation_date Example +### date Example ```json -"2019-02-14" +"2019-12-06" ``` -## data_metadata - -Information about the date being ingested - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_eventtypes Example - -```json -wineventlog -``` - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Change": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### data_models Example - -```json -Network_Resolution -``` - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_source Example - -```json -DNS -``` - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -##### data_sourcetypes Example - -```json -stream:dns -``` - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "", - "OSquery": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### providing_technologies Example - -```json -Bro -``` - - - - - - - ## description -A description of what the detection is designed to find +A detailed description of the detection `description` * is **required** * type: `string` +* default: `""` * defined in this schema ### description Type @@ -578,231 +109,19 @@ A description of what the detection is designed to find ### description Example ```json -"The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day." +"dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump." ``` -## detect - - -`detect` - -* is **required** -* type: `object` -* defined in this schema - -### detect Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `phantom`| | Optional | -| `splunk`| | Optional | -| `uba`| | Optional | - - - -#### phantom - - -`phantom` - -* is optional -* type: reference - -##### phantom Type - - -* []() – `#/definitions/phantom` - - - - - - - -#### splunk - - -`splunk` - -* is optional -* type: reference - -##### splunk Type - - -* []() – `#/definitions/splunk` - - - - - - - -#### uba - - -`uba` - -* is optional -* type: reference - -##### uba Type - - -* []() – `#/definitions/uba` - - - - - - - - - - -## eli5 - -Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert - -`eli5` - -* is **required** -* type: `string` -* defined in this schema - -### eli5 Type - - -`string` - - - - - - -### eli5 Example - -```json -"Using a lookup `discover_dns_records` generated by support search \"Discover DNS records\" we check previous network traffic and make sure the responses have not changed." -``` - - -## entities - -A list of entities that is outputed by the search... - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that is outputed by the search...", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -### entities Examples - -```json -"dest" -``` - -```json -"user" -``` - - - ## how_to_implement -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications +information about how to implement. Only needed for non standard implementations. `how_to_implement` -* is **required** +* is optional * type: `string` +* default: `""` * defined in this schema ### how_to_implement Type @@ -815,15 +134,22 @@ A discussion on how to implement this search, from what needs to be ingested, co +### how_to_implement Example + +```json +"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." +``` + ## id -The unique identifier for the detection +UUID as unique identifier `id` * is **required** * type: `string` +* default: `""` * defined in this schema ### id Type @@ -839,135 +165,19 @@ The unique identifier for the detection ### id Example ```json -"To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search \"Discover DNS record\"." +"fb4c31b0-13e8-4155-8aa5-24de4b8d6717" ``` -## investigations - -An array of the investigation objects to exectute on the detection results - -`investigations` - -* is optional -* type: `object[]` -* defined in this schema - -### investigations Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `product_type`| string | **Required** | - - - -#### id - -UUID of the investigation object - -`id` - -* is **required** -* type: `string` - -##### id Type - - -`string` - - - - - - -##### id Example - -```json -bc11a8cf-35e7-4bb2-8140-e756cc06fd72 -``` - - - - -#### name - -Name of investigation object - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Get DNS Server History for a host -``` - - - - -#### product_type - -Type of investigation object - -`product_type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#investigations-known-values). - -##### product_type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### product_type Example - -```json -splunk -``` - - - - - - - - - ## known_false_positives -Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior +known false postives `known_false_positives` * is **required** * type: `string` +* default: `""` * defined in this schema ### known_false_positives Type @@ -983,804 +193,18 @@ Scenarios in which detected behavior is benig, coupled with suggestions on how t ### known_false_positives Example ```json -"Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate." -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## mappings - -Mappings to various industry standards and frameworks - -`mappings` - -* is optional -* type: `object` -* defined in this schema - -### mappings Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cis20`| array | Optional | -| `emoji`| array | Optional | -| `kill_chain_phases`| array | Optional | -| `mitre_attack`| array | Optional | -| `nist`| array | Optional | - - - -#### cis20 - -A list of critical security controls this search helps you implement - -`cis20` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### cis20 Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of critical security controls this search helps you implement", - "examples": [ - "CIS 12" - ], - "items": { - "enum": [ - "CIS 1", - "CIS 2", - "CIS 3", - "CIS 4", - "CIS 5", - "CIS 6", - "CIS 7", - "CIS 8", - "CIS 9", - "CIS 10", - "CIS 11", - "CIS 12", - "CIS 13", - "CIS 14", - "CIS 15", - "CIS 16", - "CIS 17", - "CIS 18", - "CIS 19", - "CIS 20" - ], - "simpletype": "`enum`", - "meta:enum": { - "CIS 1": "", - "CIS 2": "", - "CIS 3": "", - "CIS 4": "", - "CIS 5": "", - "CIS 6": "", - "CIS 7": "", - "CIS 8": "", - "CIS 9": "", - "CIS 10": "", - "CIS 11": "", - "CIS 12": "", - "CIS 13": "", - "CIS 14": "", - "CIS 15": "", - "CIS 16": "", - "CIS 17": "", - "CIS 18": "", - "CIS 19": "", - "CIS 20": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### cis20 Example - -```json -CIS 12 -``` - - - - -#### emoji - -A list of security emojis that will help UBA understand this alert as an external alarm - -`emoji` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### emoji Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of security emojis that will help UBA understand this alert as an external alarm", - "examples": [ - "EndPoint" - ], - "items": { - "enum": [ - "EndPoint", - "AD", - "Firewall", - "ApplicationLog", - "IPS", - "CloudData", - "Correlation", - "Printer", - "Badge" - ], - "simpletype": "`enum`", - "meta:enum": { - "EndPoint": "", - "AD": "", - "Firewall": "", - "ApplicationLog": "", - "IPS": "", - "CloudData": "", - "Correlation": "", - "Printer": "", - "Badge": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### emoji Example - -```json -EndPoint -``` - - - - -#### kill_chain_phases - -A list of kill-chain phases to which the search applies - -`kill_chain_phases` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### kill_chain_phases Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of kill-chain phases to which the search applies", - "examples": [ - "Reconnaissance" - ], - "items": { - "enum": [ - "Reconnaissance", - "Weaponization", - "Delivery", - "Exploitation", - "Installation", - "Command and Control", - "Actions on Objectives" - ], - "simpletype": "`enum`", - "meta:enum": { - "Reconnaissance": "", - "Weaponization": "", - "Delivery": "", - "Exploitation": "", - "Installation": "", - "Command and Control": "", - "Actions on Objectives": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### kill_chain_phases Example - -```json -Reconnaissance -``` - - - - -#### mitre_attack - -A list of the techniques and tactics identified by the search - -`mitre_attack` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### mitre_attack Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of the techniques and tactics identified by the search", - "examples": [ - "Defense Evasion", - "Initial Access" - ], - "items": { - "enum": [ - "Initial Access", - "Execution", - "Persistence", - "Privilege Escalation", - "Defense Evasion", - "Credential Access", - "Discovery", - "Lateral Movement", - "Collection", - "Exfiltration", - "Command and Control", - "Command and Control Protocol", - "Commonly Used Port", - "Custom Cryptographic Protocol", - "DLL Injection", - "DLL Search Order Hijacking", - "DLL Side-Loading", - "Data Compressed", - "Data Encrypted", - "Data Obfuscation", - "Data Staged", - "Data Transfer Size Limits", - "Data from Local System", - "Data from Network Shared Drive", - "Data from Removable Media", - "Disabling Security Tools", - "Email Collection", - "Execution through API", - "Exfiltration Over Alternative Protocol", - "Exfiltration Over Command and Control Channel", - "Exfiltration Over Other Network Medium", - "Exfiltration Over Physical Medium", - "Exploitation of Vulnerability", - "Fallback Channels", - "File Deletion", - "File System Logical Offsets", - "File System Permissions Weakness", - "File and Directory Discovery", - "Graphical User Interface", - "Hypervisor", - "Indicator Blocking", - "Indicator Removal from Tools", - "Indicator Removal on Host", - "Input Capture", - "InstallUtil", - "Legitimate Credentials", - "Local Network Configuration Discovery", - "Local Network Connections Discovery", - "Local Port Monitor", - "Logon Scripts", - "MSBuild", - "Masquerading", - "Modify Existing Service", - "Modify Registry", - "Multi-Stage Channels", - "Multiband Communication", - "Multilayer Encryption", - "NTFS Extended Attributes", - "Network Service Scanning", - "Network Share Connection Removal", - "Network Sniffing", - "New Service", - "Obfuscated Files or Information", - "Pass the Hash", - "Pass the Ticket", - "Path Interception", - "Peripheral Device Discovery", - "Permission Groups Discovery", - "PowerShell", - "Process Discovery", - "Process Hollowing", - "Query Registry", - "Redundant Access", - "Registry Run Keys / Start Folder", - "Regsvcs/Regasm", - "Regsvr32", - "Remote Desktop Protocol", - "Create Account", - "Remote File Copy", - "Remote Services", - "Remote System Discovery", - "Replication Through Removable Media", - "Rootkit", - "Rundll32", - "Scheduled Task", - "Scheduled Transfer", - "Screen Capture", - "Scripting", - "Security Software Discovery", - "Security Support Provider", - "Service Execution", - "Service Registry Permissions Weakness", - "Shared Webroot", - "Shortcut Modification", - "Software Packing", - "Standard Application Layer Protocol", - "Standard Cryptographic Protocol", - "Standard Non-Application Layer Protocol", - "System Information Discovery", - "System Owner/User Discovery", - "System Service Discovery", - "System Time Discovery", - "Taint Shared Content", - "Third-party Software", - "Timestomp", - "Two-Factor Authentication Interception", - "Uncommonly Used Port", - "Video Capture", - "Valid Accounts", - "Web Service", - "Web Shell", - "Windows Admin Shares", - "Windows Management Instrumentation Event Subscription", - "Windows Management Instrumentation", - "Windows Remote Management", - "Winlogon Helper DLL", - "Exploitation for Privilege Escalation" - ], - "simpletype": "`enum`", - "meta:enum": { - "Initial Access": "", - "Execution": "", - "Persistence": "", - "Privilege Escalation": "", - "Defense Evasion": "", - "Credential Access": "", - "Discovery": "", - "Lateral Movement": "", - "Collection": "", - "Exfiltration": "", - "Command and Control": "", - "Command and Control Protocol": "", - "Commonly Used Port": "", - "Custom Cryptographic Protocol": "", - "DLL Injection": "", - "DLL Search Order Hijacking": "", - "DLL Side-Loading": "", - "Data Compressed": "", - "Data Encrypted": "", - "Data Obfuscation": "", - "Data Staged": "", - "Data Transfer Size Limits": "", - "Data from Local System": "", - "Data from Network Shared Drive": "", - "Data from Removable Media": "", - "Disabling Security Tools": "", - "Email Collection": "", - "Execution through API": "", - "Exfiltration Over Alternative Protocol": "", - "Exfiltration Over Command and Control Channel": "", - "Exfiltration Over Other Network Medium": "", - "Exfiltration Over Physical Medium": "", - "Exploitation of Vulnerability": "", - "Fallback Channels": "", - "File Deletion": "", - "File System Logical Offsets": "", - "File System Permissions Weakness": "", - "File and Directory Discovery": "", - "Graphical User Interface": "", - "Hypervisor": "", - "Indicator Blocking": "", - "Indicator Removal from Tools": "", - "Indicator Removal on Host": "", - "Input Capture": "", - "InstallUtil": "", - "Legitimate Credentials": "", - "Local Network Configuration Discovery": "", - "Local Network Connections Discovery": "", - "Local Port Monitor": "", - "Logon Scripts": "", - "MSBuild": "", - "Masquerading": "", - "Modify Existing Service": "", - "Modify Registry": "", - "Multi-Stage Channels": "", - "Multiband Communication": "", - "Multilayer Encryption": "", - "NTFS Extended Attributes": "", - "Network Service Scanning": "", - "Network Share Connection Removal": "", - "Network Sniffing": "", - "New Service": "", - "Obfuscated Files or Information": "", - "Pass the Hash": "", - "Pass the Ticket": "", - "Path Interception": "", - "Peripheral Device Discovery": "", - "Permission Groups Discovery": "", - "PowerShell": "", - "Process Discovery": "", - "Process Hollowing": "", - "Query Registry": "", - "Redundant Access": "", - "Registry Run Keys / Start Folder": "", - "Regsvcs/Regasm": "", - "Regsvr32": "", - "Remote Desktop Protocol": "", - "Create Account": "", - "Remote File Copy": "", - "Remote Services": "", - "Remote System Discovery": "", - "Replication Through Removable Media": "", - "Rootkit": "", - "Rundll32": "", - "Scheduled Task": "", - "Scheduled Transfer": "", - "Screen Capture": "", - "Scripting": "", - "Security Software Discovery": "", - "Security Support Provider": "", - "Service Execution": "", - "Service Registry Permissions Weakness": "", - "Shared Webroot": "", - "Shortcut Modification": "", - "Software Packing": "", - "Standard Application Layer Protocol": "", - "Standard Cryptographic Protocol": "", - "Standard Non-Application Layer Protocol": "", - "System Information Discovery": "", - "System Owner/User Discovery": "", - "System Service Discovery": "", - "System Time Discovery": "", - "Taint Shared Content": "", - "Third-party Software": "", - "Timestomp": "", - "Two-Factor Authentication Interception": "", - "Uncommonly Used Port": "", - "Video Capture": "", - "Valid Accounts": "", - "Web Service": "", - "Web Shell": "", - "Windows Admin Shares": "", - "Windows Management Instrumentation Event Subscription": "", - "Windows Management Instrumentation": "", - "Windows Remote Management": "", - "Winlogon Helper DLL": "", - "Exploitation for Privilege Escalation": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### mitre_attack Examples - -```json -Defense Evasion -``` - -```json -Initial Access -``` - - - - - -#### nist - -A list of the NIST controls the search helps you implement - -`nist` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### nist Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of the NIST controls the search helps you implement", - "examples": [ - "ID.AM", - "PR.PT" - ], - "items": { - "enum": [ - "ID.AM", - "ID.RA", - "PR.DS", - "PR.IP", - "PR.AC", - "PR.PT", - "PR.AT", - "PR.MA", - "DE.CM", - "DE.DP", - "DE.AE", - "RS.MI", - "RS.AN", - "RS.RP", - "RS.IM", - "RS.CO", - "RC.IM", - "RC.CO" - ], - "simpletype": "`enum`", - "meta:enum": { - "ID.AM": "", - "ID.RA": "", - "PR.DS": "", - "PR.IP": "", - "PR.AC": "", - "PR.PT": "", - "PR.AT": "", - "PR.MA": "", - "DE.CM": "", - "DE.DP": "", - "DE.AE": "", - "RS.MI": "", - "RS.AN": "", - "RS.RP": "", - "RS.IM": "", - "RS.CO": "", - "RC.IM": "", - "RC.CO": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - -##### nist Examples - -```json -ID.AM -``` - -```json -PR.PT -``` - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2019-02-14" +"Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual." ``` ## name - -The name of the detection +### Name of detection `name` -* is optional +* is **required** * type: `string` +* default: `""` * defined in this schema ### name Type @@ -1796,163 +220,20 @@ The name of the detection ### name Example ```json -"DNS record changed" -``` - - -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## product_type - -The type of detection - -`product_type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#product_type-known-values). - -### product_type Known Values -| Value | Description | -|-------|-------------| -| `uba` | | -| `splunk` | | -| `phantom` | | - - - -### product_type Example - -```json -"phantom" +"Access LSASS Memory for Dump Creation" ``` ## references -A list of URLs that give more information about the search +A list of references for this detection `references` * is optional * type: `string[]` -* at least `0` items in the array + +* default: `[]` * defined in this schema ### references Type @@ -1966,6 +247,9 @@ All items must be of the type: + +An explanation about the purpose of this instance. + @@ -1973,47 +257,85 @@ All items must be of the type: ### references Example ```json -"https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" +[ + "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf" +] ``` -## responses +## search -An array of the response objects to exectute on the detection results +The Splunk search for the detection -`responses` +`search` -* is optional -* type: `object[]` +* is **required** +* type: `string` +* default: `""` * defined in this schema -### responses Type +### search Type -Array type: `object[]` +`string` + + + + + + +### search Example + +```json +"`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`" +``` + + +## tags + +An array of key value pairs for tagging + +`tags` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### tags Type + -All items must be of the type: `object` with following properties: | Property | Type | Required | |----------|------|----------| -| `id`| string | **Required** | -| `name`| string | **Required** | -| `product_type`| | **Required** | -#### id -UUID of the Respose object +### tags Example -`id` +```json +{ + "analytics_story": "credential_dumping", + "custom_key": "custom_value" +} +``` + + +## type + +type of detection + +`type` * is **required** * type: `string` +* default: `""` +* defined in this schema -##### id Type +### type Type `string` @@ -2023,147 +345,28 @@ UUID of the Respose object -##### id Example +### type Example ```json -1169w17b-ef78-4b59-aae8-5369073014e1 -``` - - - - -#### name - -Name of Response Object - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -DNS Hijack Reponse -``` - - - - -#### product_type - -Type of baseline to execute - -`product_type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#responses-known-values). - -##### product_type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - -##### product_type Example - -```json -phantom -``` - - - - - - - - - -## security_domain - -The high-level security area to which the search belongs - -`security_domain` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#security_domain-known-values). - -### security_domain Known Values -| Value | Description | -|-------|-------------| -| `access` | | -| `endpoint` | | -| `network` | | -| `threat` | | - - - -### security_domain Example - -```json -"endpoint" -``` - - -## spec_version - -The version of the detection specification this manifest follows - -`spec_version` - -* is optional -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -"2.0" +"ESCU" ``` ## version -The version of the detection +version of detection, e.g. 1 or 2 ... `version` * is **required** -* type: `string` +* type: `integer` +* default: `0` * defined in this schema ### version Type -`string` +`integer` @@ -2173,675 +376,6 @@ The version of the detection ### version Example ```json -"1" -``` - - -# Detection Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [correlation_rule](#correlation_rule) | `object` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [event_type](#event_type) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [model](#model) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [model_version](#model_version) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/phantom` | -| [threat_category](#threat_category) | `string` | `https://api.splunkresearch.com/schemas/detections.json#/definitions/uba` | - -## correlation_rule - -Various fields to enhance usability in Enterprise Security - -`correlation_rule` - -* is optional -* type: `object` -* defined in this schema - -### correlation_rule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `notable`| object | Optional | -| `risk`| object | Optional | -| `schedule`| object | **Required** | -| `search`| string | **Required** | -| `suppress`| object | Optional | - - - -#### notable - -Various fields associated with creating a notable event - -`notable` - -* is optional -* type: `object` - -##### notable Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `nes_fields`| string | **Required** | -| `rule_description`| string | **Required** | -| `rule_title`| string | **Required** | - - - -#### nes_fields - -A list of suggested fields to be used for notable-event suppression - -`nes_fields` - -* is **required** -* type: `string` - -##### nes_fields Type - - -`string` - - - - - - - - - -#### rule_description - -Description of the notable event that will display in Incident Review - -`rule_description` - -* is **required** -* type: `string` - -##### rule_description Type - - -`string` - - - - - - - - - -#### rule_title - -Title of the notable event that will display in Incident Review - -`rule_title` - -* is **required** -* type: `string` - -##### rule_title Type - - -`string` - - - - - - - - - - - - - - -#### risk - -Fields associated with assigning risk to objects - -`risk` - -* is optional -* type: `object` - -##### risk Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `risk_object`| string | **Required** | -| `risk_object_type`| array | **Required** | -| `risk_score`| integer | **Required** | - - - -#### risk_object - -TThe field to which you are assigning risk - -`risk_object` - -* is **required** -* type: `string` - -##### risk_object Type - - -`string` - - - - - - - - - -#### risk_object_type - -The type of object to which you are assigning risk - -`risk_object_type` - -* is **required** -* type: `enum[]`* between `0` and `1` items in the array - - -##### risk_object_type Type - - -Array type: `enum[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### risk_score - -Score assigned to risk_object - -`risk_score` - -* is **required** -* type: `integer` - -##### risk_score Type - - -`integer` - - - - - - - - - - - - - - -#### schedule - -Various fields to assist in scheduling the search - -`schedule` - -* is **required** -* type: `object` - -##### schedule Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `cron_schedule`| string | Optional | -| `earliest_time`| string | Optional | -| `latest_time`| string | Optional | - - - -#### cron_schedule - -Schedule of the search in cron format - -`cron_schedule` - -* is optional -* type: `string` - -##### cron_schedule Type - - -`string` - - - - - - - - - -#### earliest_time - -The earliest time the search should run in Splunk format - -`earliest_time` - -* is optional -* type: `string` - -##### earliest_time Type - - -`string` - - - - - - - - - -#### latest_time - -The latest time tes search should run against in Splunk format - -`latest_time` - -* is optional -* type: `string` - -##### latest_time Type - - -`string` - - - - - - - - - - - - - - -#### search - -The search (in SPL) executed within core Splunk - -`search` - -* is **required** -* type: `string` - -##### search Type - - -`string` - - - - - - - - - -#### suppress - -Fields associated with suppressing the creation of multiple alerts - -`suppress` - -* is optional -* type: `object` - -##### suppress Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `suppress_fields`| string | **Required** | -| `suppress_period`| string | **Required** | - - - -#### suppress_fields - -The fields to base the suppression on - -`suppress_fields` - -* is **required** -* type: `string` - -##### suppress_fields Type - - -`string` - - - - - - - - - -#### suppress_period - -The length of time the suppression should be in effect - -`suppress_period` - -* is **required** -* type: `string` - -##### suppress_period Type - - -`string` - - - - - - - - - - - - - - - - - -## event_type - -An anomaly or threat. - -`event_type` - -* is optional -* type: `string` -* defined in this schema - -### event_type Type - - -`string` - - - - - - -### event_type Example - -```json -"anomaly" -``` - - -## model - -The name of the Splunk UBA model that detected the anomaly. - -`model` - -* is optional -* type: `string` -* defined in this schema - -### model Type - - -`string` - - - - - - -### model Example - -```json -"UBA Model" -``` - - -## model_version - -Url of the playbook on Phantom website. - -`model_version` - -* is optional -* type: `string` -* defined in this schema - -### model_version Type - - -`string` - - - - - - -### model_version Example - -```json -"2.0" -``` - - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - -### phantom_server Example - -```json -"automation (hostname)" -``` - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - -### playbook_name Example - -```json -"community/dns_hijack_detect_playbook" -``` - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - -### playbook_url Example - -```json -"https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" -``` - - -## sensitivity - -TLP colors (white, green, amber or red) - -`sensitivity` - -* is optional -* type: `string` -* defined in this schema - -### sensitivity Type - - -`string` - - - - - - -### sensitivity Example - -```json -"green" -``` - - -## severity - -Severity in phantom (High, Medium, Low) - -`severity` - -* is optional -* type: `string` -* defined in this schema - -### severity Type - - -`string` - - - - - - -### severity Example - -```json -"high" -``` - - -## threat_category - -The category of a threat in Splunk UBA. - -`threat_category` - -* is optional -* type: `string` -* defined in this schema - -### threat_category Type - - -`string` - - - - - - -### threat_category Example - -```json -"Malware" +2 ``` diff --git a/docs/spec/investigations.spec.md b/docs/spec/investigations.spec.md deleted file mode 100644 index 934ac9dc37..0000000000 --- a/docs/spec/investigations.spec.md +++ /dev/null @@ -1,915 +0,0 @@ - -# Investigative Search Manifest Schema - -``` -https://api.splunkresearch.com/schemas/investigations.json -``` - -The fields that make up the manifest of a version 2 investigative object - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | - -# Investigative Search Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [creation_date](#creation_date) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Investigative Search Manifest (this schema) | -| [definitions](#definitions) | complex | Optional | No | Investigative Search Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Investigative Search Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [investigate](#investigate) | complex | **Required** | No | Investigative Search Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Investigative Search Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| [name](#name) | `string` | Optional | No | Investigative Search Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Investigative Search Manifest (this schema) | -| [product_type](#product_type) | `enum` | **Required** | No | Investigative Search Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | **Required** | No | Investigative Search Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Investigative Search Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## creation_date - -The date the story manifest was created - -`creation_date` - -* is **required** -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - - -## data_metadata - -Information about the date being ingested - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `enum[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of data models, if any, used by this search", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ], - "simpletype": "`enum`", - "meta:enum": { - "Alerts": "", - "Application_State": "", - "Authentication": "", - "Certificates": "", - "Change_Analysis": "", - "Change": "", - "Malware": "", - "Email": "", - "Identity_Management": "", - "Network_Resolution": "", - "Network_Traffic": "", - "Vulnerabilities": "", - "Web": "", - "Network_Sessions": "", - "Updates": "", - "Risk": "", - "Endpoint": "" - } - }, - "type": "array", - "minItems": 0, - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "", - "OSquery": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - - - - - - -## definitions - - -`definitions` - -* is optional -* type: complex -* defined in this schema - -### definitions Type - -Unknown type ``. - -```json -{ - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)", - "type": "string" - }, - "playbook_display_name": { - "description": "Display Name of the playbook. Capitalize each letter and remove underscores from playbook_name field. Eg: Simple Network Enrichment", - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment", - "type": "string" - }, - "playbook_url": { - "description": "Url of the playbook on Phantom website.", - "type": "string" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber or Red)", - "type": "string" - }, - "severity": { - "description": "Severity in phantom (High, Medium, Low)", - "type": "string" - } - }, - "required": [ - "phantom_server", - "playbook_name", - "playbook_url", - "playbook_display_name" - ], - "type": "object" - }, - "splunk": { - "properties": { - "investigate_window": { - "additionalProperties": false, - "description": "The fields associated on when this search should run relative to the detection event", - "properties": { - "earliest_time_offset": { - "description": "The number of seconds into the past from the event time the search should cover", - "type": "integer" - }, - "latest_time_offset": { - "description": "The number of seconds into the future from the event time the search should cover", - "type": "integer" - } - }, - "required": [ - "latest_time_offset", - "earliest_time_offset" - ], - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within core Splunk for investgation.", - "type": "string" - } - }, - "required": [ - "search", - "investigate_window" - ], - "type": "object" - }, - "simpletype": "complex" -} -``` - - - - - -## description - -A description of what the search is designed to detect - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - - -## entities - -A list of entities that will used in the story flow or are relevant to the security investigation. - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. - -```json -{ - "description": "A list of entities that will used in the story flow or are relevant to the security investigation. ", - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} -``` - - - - - - - - -## how_to_implement - -A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications - -`how_to_implement` - -* is **required** -* type: `string` -* defined in this schema - -### how_to_implement Type - - -`string` - - - - - - - -## id - -The unique identifier for the search - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - - -## investigate - - -`investigate` - -* is **required** -* type: complex -* defined in this schema - -### investigate Type - - -**One** of the following *conditions* need to be fulfilled. - - -#### Condition 1 - - -* []() – `#/definitions/splunk` - - -#### Condition 2 - - -* []() – `#/definitions/phantom` - - - - - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - - -## name - -The name of the search - -`name` - -* is optional -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - - -## original_authors - -A list of the original authors of the search - -`original_authors` - -* is **required** -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## product_type - -Type of product that will support this investigate object. - -`product_type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#product_type-known-values). - -### product_type Known Values -| Value | Description | -|-------|-------------| -| `phantom` | | -| `splunk` | | -| `uba` | | - - - - -## spec_version - -The version of the investigative search specification this manifest follows - -`spec_version` - -* is **required** -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - - -## version - -The version of the search - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - diff --git a/docs/spec/lookups.spec.md b/docs/spec/lookups.spec.md new file mode 100644 index 0000000000..38f87220d1 --- /dev/null +++ b/docs/spec/lookups.spec.md @@ -0,0 +1,282 @@ + +# Lookup Manifest Schema + +``` +https://api.splunkresearch.com/schemas/lookups.json +``` + +A object that defines a lookup file and its properties. + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | +|----------|------------|--------|--------------|-------------------|-----------------------|------------| +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | + +# Lookup Manifest Properties + +| Property | Type | Required | Nullable | Defined by | +|----------|------|----------|----------|------------| +| [case_sensitive_match](#case_sensitive_match) | `enum` | Optional | No | Lookup Manifest (this schema) | +| [collection](#collection) | `string` | Optional | No | Lookup Manifest (this schema) | +| [default_match](#default_match) | `string` | Optional | No | Lookup Manifest (this schema) | +| [description](#description) | `string` | Optional | No | Lookup Manifest (this schema) | +| [filename](#filename) | `string` | Optional | No | Lookup Manifest (this schema) | +| [match_type](#match_type) | `string` | Optional | No | Lookup Manifest (this schema) | +| [max_matches](#max_matches) | `integer` | Optional | No | Lookup Manifest (this schema) | +| [min_matches](#min_matches) | `integer` | Optional | No | Lookup Manifest (this schema) | +| [name](#name) | `string` | Optional | No | Lookup Manifest (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | + +## case_sensitive_match + +What the macro is intended to filter + +`case_sensitive_match` + +* is optional +* type: `enum` +* defined in this schema + +The value of this property **must** be equal to one of the [known values below](#case_sensitive_match-known-values). + +### case_sensitive_match Known Values +| Value | Description | +|-------|-------------| +| `true` | | +| `false` | | + + + +### case_sensitive_match Example + +```json +"true" +``` + + +## collection + +Name of the collection to use for this lookup + +`collection` + +* is optional +* type: `string` +* defined in this schema + +### collection Type + + +`string` + + + + + + +### collection Example + +```json +"prohibited_apps_launching_cmd" +``` + + +## default_match + +The default value if no match is found + +`default_match` + +* is optional +* type: `string` +* defined in this schema + +### default_match Type + + +`string` + + + + + + +### default_match Example + +```json +"true" +``` + + +## description + +The description of this lookup + +`description` + +* is optional +* type: `string` +* defined in this schema + +### description Type + + +`string` + + + + + + +### description Example + +```json +"This lookup contains file names that exist in the Windows\\System32 directory" +``` + + +## filename + +The name of the file to use for this lookup + +`filename` + +* is optional +* type: `string` +* defined in this schema + +### filename Type + + +`string` + + + + + + +### filename Example + +```json +"prohibited_apps_launching_cmd.csv" +``` + + +## match_type + +A comma and space-delimited list of () specification to allow for non-exact matching + +`match_type` + +* is optional +* type: `string` +* defined in this schema + +### match_type Type + + +`string` + + + + + + +### match_type Example + +```json +"WILDCARD(process)" +``` + + +## max_matches + +The maximum number of possible matches for each input lookup value + +`max_matches` + +* is optional +* type: `integer` +* defined in this schema + +### max_matches Type + + +`integer` + + + + + + +### max_matches Example + +```json +"100" +``` + + +## min_matches + +Minimum number of possible matches for each input lookup value + +`min_matches` + +* is optional +* type: `integer` +* defined in this schema + +### min_matches Type + + +`integer` + + + + + + +### min_matches Example + +```json +"1" +``` + + +## name + +The name of the lookup to be used in searches + +`name` + +* is optional +* type: `string` +* defined in this schema + +### name Type + + +`string` + + + + + + +### name Example + +```json +"isWindowsSystemFile_lookup" +``` + + + +**One** of the following *conditions* need to be fulfilled. + + +#### Condition 1 + + + +#### Condition 2 + + diff --git a/docs/spec/macros.spec.md b/docs/spec/macros.spec.md new file mode 100644 index 0000000000..f39fdeffe4 --- /dev/null +++ b/docs/spec/macros.spec.md @@ -0,0 +1,131 @@ + +# Macro Manifest Schema + +``` +https://api.splunkresearch.com/schemas/macros.json +``` + +An object that defines the parameters for a Splunk Macro + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | +|----------|------------|--------|--------------|-------------------|-----------------------|------------| +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | + +# Macro Manifest Properties + +| Property | Type | Required | Nullable | Defined by | +|----------|------|----------|----------|------------| +| [arguments](#arguments) | `string[]` | Optional | No | Macro Manifest (this schema) | +| [definition](#definition) | `string` | Optional | No | Macro Manifest (this schema) | +| [description](#description) | `string` | **Required** | No | Macro Manifest (this schema) | +| [name](#name) | `string` | **Required** | No | Macro Manifest (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | + +## arguments + +A list of the arguments being passed to this macro + +`arguments` + +* is optional +* type: `string[]` +* at least `0` items in the array +* defined in this schema + +### arguments Type + + +Array type: `string[]` + +All items must be of the type: +`string` + + + + + + + + + + +## definition + +The macro definition + +`definition` + +* is optional +* type: `string` +* defined in this schema + +### definition Type + + +`string` + + + + + + +### definition Example + +```json +"(query=fls-na* AND query = www* AND query=images*)" +``` + + +## description + +What the macro is intended to filter + +`description` + +* is **required** +* type: `string` +* defined in this schema + +### description Type + + +`string` + + + + + + +### description Example + +```json +"Use this macro to filter out known good objects" +``` + + +## name + +The name of the macro + +`name` + +* is **required** +* type: `string` +* defined in this schema + +### name Type + + +`string` + + + + + + +### name Example + +```json +"detection_search_output_filter" +``` + diff --git a/docs/spec/response_tasks.spec.md b/docs/spec/response_tasks.spec.md new file mode 100644 index 0000000000..be14942e11 --- /dev/null +++ b/docs/spec/response_tasks.spec.md @@ -0,0 +1,437 @@ + +# Response Task Schema Schema + +``` +http://example.com/example.json +``` + +schema for response tasks + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | +|----------|------------|--------|--------------|-------------------|-----------------------|------------| +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | + +# Response Task Schema Properties + +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [author](#author) | `string` | **Required** | No | `""` | Response Task Schema (this schema) | +| [dashboard](#dashboard) | `string` | Optional | No | `""` | Response Task Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Response Task Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Response Task Schema (this schema) | +| [how_to_implement](#how_to_implement) | `string` | Optional | No | `""` | Response Task Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Response Task Schema (this schema) | +| [inputs](#inputs) | `array` | Optional | No | `[]` | Response Task Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Response Task Schema (this schema) | +| [playbook](#playbook) | `object` | Optional | No | `{}` | Response Task Schema (this schema) | +| [search](#search) | `string` | Optional | No | `""` | Response Task Schema (this schema) | +| [version](#version) | `integer` | **Required** | No | `0` | Response Task Schema (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | + +## author + +Author of response task + +`author` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### author Type + + +`string` + + + + + + +### author Example + +```json +"Patrick Bareiß, Splunk" +``` + + +## dashboard + +Name of dashboard used as response task + +`dashboard` + +* is optional +* type: `string` +* default: `""` +* defined in this schema + +### dashboard Type + + +`string` + + + + + + +### dashboard Example + +```json +"process_chain_analysis.json" +``` + + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### date Type + + +`string` + + + + + + +### date Example + +```json +"2019-12-06" +``` + + +## description + +Description of response task + +`description` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### description Type + + +`string` + + + + + + +### description Example + +```json +"Response Task example description" +``` + + +## how_to_implement + +information about how to implement. Only needed for non standard implementations. + +`how_to_implement` + +* is optional +* type: `string` +* default: `""` +* defined in this schema + +### how_to_implement Type + + +`string` + + + + + + +### how_to_implement Example + +```json +"This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." +``` + + +## id + +UUID as unique identifier + +`id` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### id Type + + +`string` + + + + + + +### id Example + +```json +"fb4c31b0-13e8-4155-8aa5-24de4b8d6717" +``` + + +## inputs + +Inputs used from the response task + +`inputs` + +* is optional +* type: `array` + +* default: `[]` +* defined in this schema + +### inputs Type + + +Array type: `array` + + + + +### inputs Example + +```json +[ + "lookup_file" +] +``` + + +## name + +Namo fo response task + +`name` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### name Type + + +`string` + + + + + + +### name Example + +```json +"Response Tas Example" +``` + + +## playbook + +A phantom playbook as response task + +`playbook` + +* is optional +* type: `object` +* default: `{}` +* defined in this schema + +### playbook Type + + +`object` with following properties: + + +| Property | Type | Required | Default | +|----------|------|----------|---------| +| `name`| string | **Required** | `""` | +| `url_json`| string | **Required** | `""` | +| `url_python`| string | **Required** | `""` | + + + +#### name + +Name of Phantom Playbook + +`name` + +* is **required** +* type: `string` +* default: `""` + + +##### name Type + + +`string` + + + + + + +##### name Example + +```json +lets_encrypt_domain_investigate.json +``` + + + + +#### url_json + +URL for phantom playbook json file + +`url_json` + +* is **required** +* type: `string` +* default: `""` + + +##### url_json Type + + +`string` + + + + + + +##### url_json Example + +```json +https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json +``` + + + + +#### url_python + +URL for phantom playbook python file + +`url_python` + +* is **required** +* type: `string` +* default: `""` + + +##### url_python Type + + +`string` + + + + + + +##### url_python Example + +```json +https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py +``` + + + + + +### playbook Example + +```json +{ + "name": "lets_encrypt_domain_investigate.json", + "url_json": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json", + "url_python": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py" +} +``` + + +## search + +Search as response task + +`search` + +* is optional +* type: `string` +* default: `""` +* defined in this schema + +### search Type + + +`string` + + + + + + +### search Example + +```json +"`sysmon` EventCode=1 | search [| inputlookup %lookup_file% ] | stats count by dest user process_name" +``` + + +## version + +version of detection, e.g. 1 or 2 ... + +`version` + +* is **required** +* type: `integer` +* default: `0` +* defined in this schema + +### version Type + + +`integer` + + + + + + +### version Example + +```json +3 +``` + diff --git a/docs/spec/responses.spec.md b/docs/spec/responses.spec.md index e6a345f849..55779a8dfa 100644 --- a/docs/spec/responses.spec.md +++ b/docs/spec/responses.spec.md @@ -1,47 +1,42 @@ -# Response Manifest Schema +# Response Schema Schema ``` -https://api.splunkresearch.com/schemas/investigations.json +http://example.com/example.json ``` -The fields that make up the manifest of a version 1 reponse spec +schema for response | Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | |----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | Yes | Experimental | No | Forbidden | Forbidden | | +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | -# Response Manifest Properties +# Response Schema Properties -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [creation_date](#creation_date) | `string` | **Required** | No | Response Manifest (this schema) | -| [data_metadata](#data_metadata) | `object` | **Required** | No | Response Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Response Manifest (this schema) | -| [entities](#entities) | `enum[]` | Optional | No | Response Manifest (this schema) | -| [fields_required](#fields_required) | `string[]` | Optional | No | Response Manifest (this schema) | -| [how_to_implement](#how_to_implement) | `string` | **Required** | No | Response Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Response Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | **Required** | No | Response Manifest (this schema) | -| [modification_date](#modification_date) | `string` | **Required** | No | Response Manifest (this schema) | -| [name](#name) | `string` | Optional | No | Response Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | **Required** | No | Response Manifest (this schema) | -| [product_type](#product_type) | `enum` | **Required** | No | Response Manifest (this schema) | -| [response](#response) | complex | **Required** | No | Response Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | **Required** | No | Response Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Response Manifest (this schema) | +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [author](#author) | `string` | **Required** | No | `""` | Response Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Response Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Response Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Response Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Response Schema (this schema) | +| [response_tasks](#response_tasks) | `array` | **Required** | No | `{}` | Response Schema (this schema) | +| [tags](#tags) | `object` | **Required** | No | `{}` | Response Schema (this schema) | +| [version](#version) | `integer` | **Required** | No | `0` | Response Schema (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | -## creation_date +## author -The date the story manifest was created +Author of the response -`creation_date` +`author` * is **required** * type: `string` +* default: `""` * defined in this schema -### creation_date Type +### author Type `string` @@ -51,248 +46,50 @@ The date the story manifest was created - -## data_metadata - -Information about the date being used to run the response - -`data_metadata` - -* is **required** -* type: `object` -* defined in this schema - -### data_metadata Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `data_eventtypes`| array | Optional | -| `data_models`| array | Optional | -| `data_source`| array | **Required** | -| `data_sourcetypes`| array | Optional | -| `providing_technologies`| array | **Required** | - - - -#### data_eventtypes - -A list of eventtypes, if any, used by this search - -`data_eventtypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_eventtypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_models - -A list of data models, if any, used by this search - -`data_models` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_models Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_source - -A high-level description of the type of data needed for this search to complete - -`data_source` - -* is **required** -* type: `string[]`* at least `0` items in the array - - -##### data_source Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### data_sourcetypes - -The list of sourcetypes, if any, used by this search - -`data_sourcetypes` - -* is optional -* type: `string[]`* at least `0` items in the array - - -##### data_sourcetypes Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - - - -#### providing_technologies - -A list of technologies that provide this data - -`providing_technologies` - -* is **required** -* type: `enum[]`* at least `0` items in the array - - -##### providing_technologies Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. +### author Example ```json -{ - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften" - ], - "simpletype": "`enum`", - "meta:enum": { - "Apache": "", - "AWS": "", - "Bro": "", - "Microsoft Windows": "", - "Linux": "", - "macOS": "", - "Netbackup": "", - "Splunk Enterprise": "", - "Splunk Enterprise Security": "", - "Splunk Stream": "", - "Active Directory": "", - "Bluecoat": "", - "Carbon Black Response": "", - "Carbon Black Protect": "", - "CrowdStrike Falcon": "", - "Microsoft Exchange": "", - "Nessus": "", - "Palo Alto Firewall": "", - "Qualys": "", - "Sysmon": "", - "Tanium": "", - "Ziften": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} +"Rico Valdez, Patrick Bareiß, Splunk" ``` +## date + +version of detection, e.g. 1 or 2 ... + +`date` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### date Type + + +`string` +### date Example - - +```json +"2019-12-06" +``` ## description -A description of what this reponse object will do +Description of response `description` * is **required** * type: `string` +* default: `""` * defined in this schema ### description Type @@ -305,153 +102,22 @@ A description of what this reponse object will do - -## entities - -A list of entities that is either an input or an output for the security workflow. - -`entities` - -* is optional -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### entities Type - - -Array type: `enum[]` - -All items must be of the type: -Unknown type ``. +### description Example ```json -{ - "description": "A list of entities that is either an input or an output for the security workflow.", - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user" - ], - "simpletype": "`enum`", - "meta:enum": { - "accessKeyId": "", - "arn": "", - "awsRegion": "", - "bucketName": "", - "City": "", - "Country": "", - "dest_port": "", - "dest": "", - "event_id": "", - "instanceId": "", - "message_id": "", - "networkAclId": "", - "process_name": "", - "process": "", - "recipient": "", - "Region": "", - "resourceId": "", - "session_id": "", - "src_ip": "", - "src_mac": "", - "src_user": "", - "src": "", - "user": "" - } - }, - "minItems": 0, - "type": "array", - "uniqueItems": true, - "simpletype": "`enum[]`" -} +"Response example." ``` - - - - - - -## fields_required - -A list of fields that need to be in the result of the detection search for the search to be successful - -`fields_required` - -* is optional -* type: `string[]` -* at least `0` items in the array -* defined in this schema - -### fields_required Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - - - -## how_to_implement - -A discussion on how to implement this reponse object, the config files, etc - -`how_to_implement` - -* is **required** -* type: `string` -* defined in this schema - -### how_to_implement Type - - -`string` - - - - - - - ## id -The unique identifier for the search +UUID as unique identifier `id` * is **required** * type: `string` +* default: `""` * defined in this schema ### id Type @@ -464,135 +130,22 @@ The unique identifier for the search +### id Example -## maintainers - -An array of the current maintainers of the reponse spec - -`maintainers` - -* is **required** -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person maintaining this search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person maintaining this search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## modification_date - -The date of the most recent modification to the search - -`modification_date` - -* is **required** -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - +```json +"fb4c31b0-13e8-4155-8aa5-24de4b8d6717" +``` ## name -The name of the search +Name of response `name` -* is optional +* is **required** * type: `string` +* default: `""` * defined in this schema ### name Type @@ -605,248 +158,103 @@ The name of the search +### name Example -## original_authors +```json +"Response Example" +``` -A list of the original authors of the reponse object -`original_authors` +## response_tasks + +Response tasks divided into phases + +`response_tasks` * is **required** -* type: `object[]` +* type: `array` +* at least `1` items in the array +* default: `{}` * defined in this schema -### original_authors Type +### response_tasks Type -Array type: `object[]` +Array type: `array` + + + + +### response_tasks Example + +```json +{ + "another_phase": [ + { + "id": "7c72d944-3995-4485-8e57-67b4c353989b", + "name": "Another investigation" + } + ], + "identification": [ + { + "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892", + "name": "Investigate Indicator of Compromise Hash" + }, + { + "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0", + "name": "Investigate Domains" + } + ] +} +``` + + +## tags + +An array of key value pairs for tagging + +`tags` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### tags Type + -All items must be of the type: `object` with following properties: | Property | Type | Required | |----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the search - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - - - - -#### email - -Email address of the person who originally authored the search - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - - - - -#### name - -Name of the person who originally authored the search - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - - - - - - - - - - -## product_type - -The type of detection - -`product_type` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#product_type-known-values). - -### product_type Known Values -| Value | Description | -|-------|-------------| -| `splunk` | | -| `phantom` | | - - - - -## response - - -`response` - -* is **required** -* type: complex -* defined in this schema - -### response Type - - -**One** of the following *conditions* need to be fulfilled. - - -#### Condition 1 - - -* []() – `#/definitions/splunk` - - -#### Condition 2 - - -* []() – `#/definitions/phantom` - - - - - - -## spec_version - -The version of the investigative search specification this manifest follows - -`spec_version` - -* is **required** -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` +### tags Example +```json +{ + "analytics_story": "credential_dumping" +} +``` ## version -The version of the search +version of detection, e.g. 1 or 2 ... `version` * is **required** -* type: `string` +* type: `integer` +* default: `0` * defined in this schema ### version Type -`string` - - - - - - - -# Response Manifest Definitions - -| Property | Type | Group | -|----------|------|-------| -| [investigate_window](#investigate_window) | `object` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/splunk` | -| [phantom_server](#phantom_server) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [playbook_display_name](#playbook_display_name) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [playbook_name](#playbook_name) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [playbook_url](#playbook_url) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [search](#search) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/splunk` | -| [sensitivity](#sensitivity) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | -| [severity](#severity) | `string` | `https://api.splunkresearch.com/schemas/investigations.json#/definitions/phantom` | - -## investigate_window - -The fields associated on when this search should run relative to the detection event - -`investigate_window` - -* is optional -* type: `object` -* defined in this schema - -### investigate_window Type - - -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `earliest_time_offset`| integer | **Required** | -| `latest_time_offset`| integer | **Required** | - - - -#### earliest_time_offset - -The number of seconds into the past from the event time the search should cover - -`earliest_time_offset` - -* is **required** -* type: `integer` - -##### earliest_time_offset Type - - `integer` @@ -854,177 +262,9 @@ The number of seconds into the past from the event time the search should cover +### version Example - - -#### latest_time_offset - -The number of seconds into the future from the event time the search should cover - -`latest_time_offset` - -* is **required** -* type: `integer` - -##### latest_time_offset Type - - -`integer` - - - - - - - - - - - - -## phantom_server - -IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname) - -`phantom_server` - -* is optional -* type: `string` -* defined in this schema - -### phantom_server Type - - -`string` - - - - - - - -## playbook_display_name - -Display Name of the playbook. Capitalize each letter and remove underscores from playbook_name field. Eg: Simple Network Enrichment - -`playbook_display_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_display_name Type - - -`string` - - - - - - - -## playbook_name - -Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment - -`playbook_name` - -* is optional -* type: `string` -* defined in this schema - -### playbook_name Type - - -`string` - - - - - - - -## playbook_url - -Url of the playbook on Phantom website. - -`playbook_url` - -* is optional -* type: `string` -* defined in this schema - -### playbook_url Type - - -`string` - - - - - - - -## search - -A reponse action exectued in splunk - -`search` - -* is optional -* type: `string` -* defined in this schema - -### search Type - - -`string` - - - - - - - -## sensitivity - -TLP colors (White, Green, Amber or Red) - -`sensitivity` - -* is optional -* type: `string` -* defined in this schema - -### sensitivity Type - - -`string` - - - - - - - -## severity - -Severity in phantom (High, Medium, Low) - -`severity` - -* is optional -* type: `string` -* defined in this schema - -### severity Type - - -`string` - - - - - +```json +1 +``` diff --git a/docs/spec/stories.spec.md b/docs/spec/stories.spec.md new file mode 100644 index 0000000000..722c1ac936 --- /dev/null +++ b/docs/spec/stories.spec.md @@ -0,0 +1,283 @@ + +# Analytics Story Schema Schema + +``` +http://example.com/example.json +``` + +schema analytics story + +| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | +|----------|------------|--------|--------------|-------------------|-----------------------|------------| +| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | + +# Analytics Story Schema Properties + +| Property | Type | Required | Nullable | Default | Defined by | +|----------|------|----------|----------|---------|------------| +| [author](#author) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [date](#date) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [description](#description) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [id](#id) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [name](#name) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [narrative](#narrative) | `string` | **Required** | No | `""` | Analytics Story Schema (this schema) | +| [search](#search) | `string` | Optional | No | `""` | Analytics Story Schema (this schema) | +| [tags](#tags) | `object` | **Required** | No | `{}` | Analytics Story Schema (this schema) | +| [version](#version) | `integer` | **Required** | No | `0` | Analytics Story Schema (this schema) | +| `*` | any | Additional | Yes | this schema *allows* additional properties | + +## author + +Author of the analytics story + +`author` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### author Type + + +`string` + + + + + + +### author Example + +```json +"Rico Valdez, Patrick Bareiß, Splunk" +``` + + +## date + +date of creation or modification, format yyyy-mm-dd + +`date` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### date Type + + +`string` + + + + + + +### date Example + +```json +"2019-12-06" +``` + + +## description + +description of the analytics story + +`description` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### description Type + + +`string` + + + + + + +### description Example + +```json +"Uncover activity consistent with credential dumping, a technique where attackers compromise systems and attempt to obtain and exfiltrate passwords." +``` + + +## id + +UUID as unique identifier + +`id` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### id Type + + +`string` + + + + + + +### id Example + +```json +"fb4c31b0-13e8-4155-8aa5-24de4b8d6717" +``` + + +## name + +Name of the Analytics Story + +`name` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### name Type + + +`string` + + + + + + +### name Example + +```json +"Credential Dumping" +``` + + +## narrative + +narrative of the analytics story + +`narrative` + +* is **required** +* type: `string` +* default: `""` +* defined in this schema + +### narrative Type + + +`string` + + + + + + +### narrative Example + +```json +"gathering credentials from a target system, often hashed or encrypted, is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems." +``` + + +## search + +An additional Splunk search, which uses the result of the detections + +`search` + +* is optional +* type: `string` +* default: `""` +* defined in this schema + +### search Type + + +`string` + + + + + + +### search Example + +```json +"index=asx mitre_id=t1003 | stats values(source) as detections values(process) as processes values(user) as users values(_time) as time count by dest" +``` + + +## tags + +An explanation about the purpose of this instance. + +`tags` + +* is **required** +* type: `object` +* default: `{}` +* defined in this schema + +### tags Type + + +`object` with following properties: + + +| Property | Type | Required | +|----------|------|----------| + + + + +### tags Example + +```json +{ + "analytics_story": "credential_dumping" +} +``` + + +## version + +version of analytics story, e.g. 1 or 2 ... + +`version` + +* is **required** +* type: `integer` +* default: `0` +* defined in this schema + +### version Type + + +`integer` + + + + + + +### version Example + +```json +1 +``` + diff --git a/docs/spec/story.spec.md b/docs/spec/story.spec.md deleted file mode 100644 index 518fed76d7..0000000000 --- a/docs/spec/story.spec.md +++ /dev/null @@ -1,725 +0,0 @@ - -# Analytic Story Manifest Schema - -``` -https://api.splunkresearch.com/schemas/story.json -``` - -The fields that make up the manifest of a version 2 Analytic Story - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | - -# Analytic Story Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [category](#category) | `enum[]` | **Required** | No | Analytic Story Manifest (this schema) | -| [channel](#channel) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [creation_date](#creation_date) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [detections](#detections) | `object[]` | **Required** | No | Analytic Story Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | Optional | No | Analytic Story Manifest (this schema) | -| [modification_date](#modification_date) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [name](#name) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [narrative](#narrative) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | Optional | No | Analytic Story Manifest (this schema) | -| [references](#references) | `string[]` | Optional | No | Analytic Story Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | Optional | No | Analytic Story Manifest (this schema) | -| [usecase](#usecase) | `enum` | **Required** | No | Analytic Story Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## category - -The category to which the Analytic Story belongs - -`category` - -* is **required** -* type: `enum[]` -* at least `0` items in the array -* defined in this schema - -### category Type - - -Array type: `enum[]` - -All items must be of the type: -`string` - - - - - - - - - - -## channel - -A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Content Updates are in the ESCU channel - -`channel` - -* is optional -* type: `string` -* defined in this schema - -### channel Type - - -`string` - - - - - - -### channel Example - -```json -"ESCU" -``` - - -## creation_date - -The date this story was created - -`creation_date` - -* is optional -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - -### creation_date Example - -```json -"2017-11-21" -``` - - -## description - -A high-level description or goal of the Analytic Story - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - -### description Example - -```json -"Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records." -``` - - -## detections - -An array of detection mechanisms from Splunk, UBA and phantom. - -`detections` - -* is **required** -* type: `object[]` -* defined in this schema - -### detections Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `detection_id`| string | **Required** | -| `name`| string | **Required** | -| `type`| string | **Required** | - - - -#### detection_id - -unique identifier of the detection, in the form of UUID - -`detection_id` - -* is **required** -* type: `string` - -##### detection_id Type - - -`string` - - - - - - -##### detection_id Example - -```json -1169w17b-ef78-4b59-aae8-5369073014e1 -``` - - - - -#### name - -Name of the Detection. It can be a name of a Splunk correlation search name, a UBA threat or a Phantom detection playbook. - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -DNS record changed -``` - - - - -#### type - -What product gives you a detection - -`type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#detections-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `splunk` | | -| `uba` | | -| `phantom` | | - - - -##### type Examples - -```json -splunk -``` - -```json -uba -``` - -```json -phantom -``` - - - - - - - - - - -## id - -A unique identifier for the Analytic Story - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - -### id Example - -```json -"8169f17b-ef68-4b59-aae8-5869073014e1" -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is optional -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this Analytic Story - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this Analytic Story - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this Analytic Story - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## modification_date - -The date of the most recent modification to this Analytic Story - -`modification_date` - -* is optional -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2018-09-06" -``` - - -## name - -The name of the Analytic Story - -`name` - -* is **required** -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - -### name Example - -```json -"DNS Hijacking" -``` - - -## narrative - -Long-form text that describes the Analytic Story and the rationale behind it, as well as an overview of the included searches, and how they enable the story - -`narrative` - -* is optional -* type: `string` -* defined in this schema - -### narrative Type - - -`string` - - - - - - -### narrative Example - -```json -"Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.

The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous." -``` - - -## original_authors - -An array of the original authors of the Analytic Story - -`original_authors` - -* is optional -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the Analytic Story - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the Analytic Story - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the Analytic Story - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## references - -An array of URLs that give information about the problem the story is addressing - -`references` - -* is optional -* type: `string[]` -* at least `0` items in the array -* defined in this schema - -### references Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -### references Example - -```json -"https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" -``` - - -## spec_version - -The version of the Analytic Story specification this manifest follows - -`spec_version` - -* is optional -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -2 -``` - - -## usecase - -The usecase to which the Analytic Story belongs to. - -`usecase` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#usecase-known-values). - -### usecase Known Values -| Value | Description | -|-------|-------------| -| `Security Monitoring` | | -| `Advanced Threat Detection` | | -| `Insider Threat` | | -| `Compliance` | | -| `Fraud Detection` | | - - - -### usecase Example - -```json -"Advanced Threat Detection" -``` - - -## version - -The version of the Analytic Story, bump the version number when the story gets an update - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - -### version Example - -```json -"1.0" -``` - diff --git a/docs/splunk_docs_categories.wiki b/docs/splunk_docs_categories.wiki index 3a12c11dc9..e22c687eac 100644 --- a/docs/splunk_docs_categories.wiki +++ b/docs/splunk_docs_categories.wiki @@ -2,1371 +2,6 @@ The collapse... -==Cloud Security== - -===AWS Cross Account Activity=== - -Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. - -
-
- -====Narrative==== -Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ -Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ -This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ - -creation_date = 2018-06-04 - -modification_date = 2018-06-04 - -version = 1.0 - -
-
- -===AWS Cryptomining=== - -Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. - -
-
- -====Narrative==== -Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ -hen malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So, it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ -This Analytic Story is focused on detecting suspicious new instances in your EC2 environment to help prevent such a disaster. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -creation_date = 2018-03-08 - -modification_date = 2018-03-08 - -version = 1.0 - -
-
- -===AWS Network ACL Activity=== - -Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. - -
-
- -====Narrative==== -AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html -* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ - -creation_date = 2018-01-10 - -modification_date = 2018-05-21 - -version = 2.0 - -
-
- -===AWS Suspicious Provisioning Activities=== - -Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. - -
-
- -====Narrative==== -Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ -This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -creation_date = 2018-03-16 - -modification_date = 2018-03-16 - -version = 1.0 - -
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- -===AWS User Monitoring=== - -Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. - -
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- -====Narrative==== -It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ -In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ -Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ -The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -* https://redlock.io/blog/cryptojacking-tesla - -creation_date = 2018-03-12 - -modification_date = 2018-03-12 - -version = 1.0 - -
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- -===Cloud Cryptomining=== - -Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. - -
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- -====Narrative==== -Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ -When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ -This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. - -====Providing Technologies==== -* AWS -* Azure -* GCP - -====Data Models==== -* Cloud_Infrastructure - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -creation_date = 2019-10-02 - -modification_date = 2019-10-02 - -version = 1.0 - -
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- -===Container Implantation Monitoring & Investigation=== - -Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. - -
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- -====Narrative==== -Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. - -====Providing Technologies==== -* GCP -* AWS - -====Data Models==== -* Cloud_Infrastructure - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://github.com/splunk/cloud-datamodel-security-research - -creation_date = 2020-02-20 - -modification_date = 2020-02-20 - -version = 1.0 - -
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- -===Kubernetes Scanning Activity=== - -This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. - -
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- -====Narrative==== -Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. - -====Providing Technologies==== -* AWS -* GCP - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://github.com/splunk/cloud-datamodel-security-research - -creation_date = 2020-03-24 - -modification_date = 2020-04-15 - -version = 1.0 - -
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- -===Suspicious AWS EC2 Activities=== - -Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. - -
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- -====Narrative==== -AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -creation_date = 2018-02-09 - -modification_date = 2018-02-09 - -version = 1.0 - -
-
- -===Suspicious AWS Login Activities=== - -Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. - -
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- -====Narrative==== -It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html - -creation_date = 2018-02-24 - -modification_date = 2019-05-01 - -version = 1.0 - -
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- -===Suspicious AWS S3 Activities=== - -Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. - -
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- -====Narrative==== -As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ -Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ -Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ - -creation_date = 2018-06-25 - -modification_date = 2018-07-24 - -version = 2.0 - -
-
- -===Suspicious AWS Traffic=== - -Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). - -
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- -====Narrative==== -A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ -Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ - Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ -The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ - -creation_date = 2018-05-07 - -modification_date = 2018-05-07 - -version = 1.0 - -
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- -===Unusual AWS EC2 Modifications=== - -Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. - -
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- -====Narrative==== -A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ - Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. - -====Providing Technologies==== -* AWS - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -creation_date = 2018-04-09 - -modification_date = 2018-04-09 - -version = 1.0 - -
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- - -==Best Practices== - -===Account Monitoring and Controls=== - -A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. - -
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- -====Narrative==== -Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. - -====Providing Technologies==== -* Microsoft Windows -* Active Directory - -====Data Models==== -* Change -* Identity_Management - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf - -creation_date = 2017-08-05 - -modification_date = 2017-09-06 - -version = 1.0 - -
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- -===Asset Tracking=== - -Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. - -
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- -====Narrative==== -This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. - -====Providing Technologies==== -* Splunk Stream -* Bro - -====Data Models==== -* Network_Sessions - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ - -creation_date = 2017-06-01 - -modification_date = 2017-09-13 - -version = 1.0 - -
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- -===Monitor Backup Solution=== - -Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. - -
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- -====Narrative==== -Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. - -====Providing Technologies==== -* Netbackup - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ - -creation_date = 2017-06-15 - -modification_date = 2017-09-12 - -version = 1.0 - -
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- -===Monitor for Unauthorized Software=== - -Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. - -
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- -====Narrative==== -It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ -It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ - -creation_date = 2017-06-26 - -modification_date = 2017-09-15 - -version = 1.0 - -
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- -===Monitor for Updates=== - -Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. - -
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- -====Narrative==== -It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ -Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ -Microsoft releases updates for Windows systems on a monthly cadence. They should be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed. - -====Providing Technologies==== -* Microsoft Windows - -====Data Models==== -* Updates - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://learn.cisecurity.org/20-controls-download - -creation_date = 2017-08-15 - -modification_date = 2017-09-15 - -version = 1.0 - -
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- -===Prohibited Traffic Allowed or Protocol Mismatch=== - -Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. - -
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- -====Narrative==== -A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. - -====Providing Technologies==== -* Splunk Stream -* Bro -* Palo Alto Firewall - -====Data Models==== -* Network_Resolution -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ - -creation_date = 2017-04-18 - -modification_date = 2017-09-11 - -version = 1.0 - -
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- -===Router & Infrastructure Security=== - -Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. - -
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- -====Narrative==== -Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ -This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. - -====Providing Technologies==== -* Active Directory -* Palo Alto Firewall - -====Data Models==== -* Authentication - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html -* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html - -creation_date = 2017-06-01 - -modification_date = 2017-09-12 - -version = 1.0 - -
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- -===Use of Cleartext Protocols=== - -Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. - -
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- -====Narrative==== -Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. - -====Providing Technologies==== -* Splunk Stream -* Bro - -====Data Models==== -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.monkey.org/~dugsong/dsniff/ - -creation_date = 2016-09-13 - -modification_date = 2017-09-15 - -version = 1.0 - -
-
- - -==Vulnerability== - -===Apache Struts Vulnerability=== - -Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. - -
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- -====Narrative==== -In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ -The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ -This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ -The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ -First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ -hen looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ -Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ -Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ -Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ -hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ -hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ -In the event that a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that have the file open, what processes created and/or modified the file, and the number of systems that may have this file can help to determine if the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes quickly help determine whether it is malicious in nature.\ -Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, there may be activity initiated via a compromised website the user visited.\ -It can also be very helpful to examine various behaviors of the process of interest or the parent of the process that is of interest. For example, if it turns out that the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might also be worth further scrutiny. If a process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. - -====Providing Technologies==== -* Splunk Stream -* Bro -* Bluecoat -* Apache -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf - -creation_date = 2017-03-14 - -modification_date = 2018-12-06 - -version = 1.0 - -
-
- -===JBoss Vulnerability=== - -In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. - -
-
- -====Narrative==== -This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ -It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ -hen looking at the target of the behavior uncovered by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to identify other recent events involving the target. This can help tie different events together and give further situational awareness regarding the target host.\ -Various types of information for external systems should be reviewed and, potentially, collected if the incident is, indeed, judged to be malicious. This data may be useful for generating your own threat intelligence, so you can create future alerts.\ -The following factors may assist you in determining whether the event is malicious: \ -1. Country of origin\ -1. Responsible party\ -1. Fully qualified domain names associated with the external IP address\ -1. Registration of fully qualified domain names associated with external IP address Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you qualify and understand the event and possible motivation for the attack. In addition, there are various sources that may provide reputation information on the IP address or domain name, which can assist you in determining whether the event is malicious in nature. Finally, determining whether there are other events associated with the IP address may help connect data points or expose other historic events that might be brought back into scope.\ -Gathering various data on the system of interest can sometimes help quickly determine whether something suspicious is happening. Some of these items include determining who else may have logged into the system recently, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and/or whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ -hen a specific service or application is targeted, it is often helpful to know the associated version, to help determine whether it is vulnerable to a specific exploit.\ -If you suspect an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ -If a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that opened the file, the processes that may have created and/or modified the file, and how many other systems potentially have this file can you determine whether the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes help you quickly determine if it is malicious in nature.\ -Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if svchost.exe is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. \ -It can also be helpful to examine various behaviors of and the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see whether the parent process spawned other processes that might also warrant further scrutiny. If a process is suspect, a review of the network connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. - -====Providing Technologies==== -* Splunk Stream -* Palo Alto Firewall -* Apache -* Bro - -====Data Models==== -* Web - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html - -creation_date = 2016-10-04 - -modification_date = 2017-09-14 - -version = 1.0 - -
-
- -===Spectre And Meltdown Vulnerabilities=== - -Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. - -
-
- -====Narrative==== -Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. - -====Providing Technologies==== -* Nessus -* Qualys - -====Data Models==== -* Vulnerabilities - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://meltdownattack.com/ - -creation_date = 2018-01-08 - -modification_date = 2018-01-08 - -version = 1.0 - -
-
- -===Splunk Enterprise Vulnerability=== - -Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. - -
-
- -====Narrative==== -This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ -\ -1. Splunk Enterprise 6.4.x, prior to 6.4.3\ -1. Splunk Enterprise 6.3.x, prior to 6.3.6\ -1. Splunk Enterprise 6.2.x, prior to 6.2.10\ -1. Splunk Enterprise 6.1.x, prior to 6.1.11\ -1. Splunk Enterprise 6.0.x, prior to 6.0.12\ -1. Splunk Enterprise 5.0.x, prior to 5.0.16\ -1. Splunk Light, prior to 6.4.3CVE-2016-4859 allows attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. (Credit: Noriaki Iwasaki, Cyber Defense Institute, Inc.).\ -It is important to ensure that your Splunk deployment is being kept up to date and is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites. - -====Providing Technologies==== -* Splunk Enterprise - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* http://www.splunk.com/view/SP-CAAAPQ6#announce -* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 - -creation_date = 2016-09-13 - -modification_date = 2017-09-19 - -version = 1.0 - -
-
- -===Splunk Enterprise Vulnerability CVE-2018-11409=== - -Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. - -
-
- -====Narrative==== -Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ -Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ -Read more in Splunk's official response: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings.\ -A detection search within this Analytic Story looks for vulnerabilities described in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip. - -====Providing Technologies==== -* Splunk Enterprise - -====Data Models==== - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 -* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings -* https://www.exploit-db.com/exploits/44865/ - -creation_date = 2018-06-14 - -modification_date = 2018-06-14 - -version = 1.0 - -
-
- - ==Abuse== ===Brand Monitoring=== @@ -1381,49 +16,87 @@ While you can educate your users and customers about the risks and threats posed You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ Notable events will include IP addresses, URLs, and user data. Drilling down can provide you with even more actionable intelligence, including likely geographic information, contextual searches to help you scope the problem, and investigative searches. -====Providing Technologies==== -* Splunk Stream -* Bro -* Microsoft Exchange -* Bluecoat -* Palo Alto Firewall +====Detections==== +* Monitor DNS For Brand Abuse +* Monitor Email For Brand Abuse +* Monitor Web Traffic For Brand Abuse ====Data Models==== -* Network_Resolution * Email +* Network_Resolution * Web -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives +* Delivery =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 7 =====NIST===== * PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM ====References==== * https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ * https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ * https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ -creation_date = 2017-06-01 +date = 2017-12-19 -modification_date = 2017-12-19 +version = 1 -version = 1.0 + + + +===Data Protection=== + +Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. + +
+
+ +====Narrative==== +Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. + +====Detections==== +* Detect USB device insertion +* Detect hosts connecting to dynamic domain providers +* Detection of DNS Tunnels + +====Data Models==== +* Change_Analysis +* Network_Resolution + +====Tags==== + +=====ATT&CK===== +* T1043 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Installation + +=====CIS===== +* CIS 13 + +=====NIST===== +* DE.CM +* PR.DS +* PR.PT + +====References==== +* https://www.cisecurity.org/controls/data-protection/ +* https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ + +date = 2017-09-14 + +version = 1
@@ -1439,97 +112,32 @@ DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it respond The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. -====Providing Technologies==== -* Splunk Stream -* Bro +====Detections==== +* Large Volume of DNS ANY Queries ====Data Models==== * Network_Resolution -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== -* CIS 3 -* CIS 8 * CIS 12 =====NIST===== * PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM ====References==== * https://www.us-cert.gov/ncas/alerts/TA13-088A * https://www.imperva.com/learn/application-security/dns-amplification/ -creation_date = 2016-08-24 +date = 2016-09-13 -modification_date = 2016-09-13 - -version = 1.0 - - - - -===Data Protection=== - -Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. - -
-
- -====Narrative==== -Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. - -====Providing Technologies==== -* Microsoft Windows -* Splunk Stream -* Bro - -====Data Models==== -* Change_Analysis -* Network_Resolution - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.cisecurity.org/controls/data-protection/ -* https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 -* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ - -creation_date = 2017-06-01 - -modification_date = 2017-09-14 - -version = 1.0 +version = 1
@@ -1544,46 +152,44 @@ Detect evidence of tactics used to redirect traffic from a host to a destination ====Narrative==== Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. -====Providing Technologies==== -* Splunk Stream -* Bro -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon +====Detections==== +* Clients Connecting to Multiple DNS Servers +* DNS Query Requests Resolved by Unauthorized DNS Servers +* Windows hosts file modification ====Data Models==== * Network_Resolution -* Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1048 =====Kill Chain Phases===== * Command and Control =====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* ID.AM +* PR.AC +* PR.DS +* PR.IP +* PR.PT ====References==== * https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ -creation_date = 2017-06-18 +date = 2017-09-14 -modification_date = 2017-09-14 - -version = 1.0 +version = 1 @@ -1599,47 +205,37 @@ Detect activities and various techniques associated with the abuse of `netsh.exe It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Processes created by netsh +* Processes launching netsh ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1059 +* T1089 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== -* CIS 3 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE * DE.CM +* PR.PT ====References==== * https://technet.microsoft.com/library/bb490939.aspx * https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html * http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html -creation_date = 2017-01-04 +date = 2017-01-05 -modification_date = 2017-01-05 - -version = 1.0 +version = 1 @@ -1659,690 +255,38 @@ The account-harvesting search focuses on web pages used for user-account registr The anomalous clickspeed search looks for users who are moving through your website at a faster-than-normal speed or with a perfect click cadence (high periodicity or low standard deviation), which could indicate that the user is a script, not an actual human.\ Another search detects incidents wherein a single password is used across multiple accounts, which may indicate that a fraudster has infiltrated your environment and embedded a common password within a script. -====Providing Technologies==== -* Splunk Stream +====Detections==== +* Web Fraud - Account Harvesting +* Web Fraud - Anomalous User Clickspeed +* Web Fraud - Password Sharing Across Accounts ====Data Models==== -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1078 +* T1136 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 16 +* CIS 6 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* DE.DP ====References==== * https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud * https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 -creation_date = 2018-07-12 +date = 2018-10-08 -modification_date = 2018-10-08 - -version = 1.0 - - - - - -==Malware== - -===ColdRoot MacOS RAT=== - -Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. - -
-
- -====Narrative==== -Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ -This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ -Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. - -====Providing Technologies==== -* OSquery - -====Data Models==== -* Alerts - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ -* https://objective-see.com/blog/blog_0x2A.html -* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ - -creation_date = 2019-01-29 - -modification_date = 2019-01-09 - -version = 1.0 - -
-
- -===DHS Report TA18-074A=== - -Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. - -
-
- -====Narrative==== -The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ -There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ -One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ -Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Windows -* Bro -* Splunk Stream - -====Data Models==== -* Endpoint -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.us-cert.gov/ncas/alerts/TA18-074A - -creation_date = 2018-03-19 - -modification_date = 2020-01-22 - -version = 2.0 - -
-
- -===Dynamic DNS=== - -Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. - -
-
- -====Narrative==== -Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. - -====Providing Technologies==== -* Splunk Stream -* Bro -* Bluecoat -* Palo Alto Firewall - -====Data Models==== -* Network_Resolution -* Web - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html -* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ -* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ -* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html - -creation_date = 2017-11-21 - -modification_date = 2018-09-06 - -version = 2.0 - -
-
- -===Emotet Malware (DHS Report TA18-201A)=== - -Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. - -
-
- -====Narrative==== -The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ -According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ -The searches in this Analytic Story will help you find executables that are rarely used in your environment, specific registry paths that malware often uses to ensure survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that Emotet or other malware has compromised your environment. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Exchange -* Bro -* Splunk Stream - -====Data Models==== -* Endpoint -* Email -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.us-cert.gov/ncas/alerts/TA18-201A -* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf -* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html - -creation_date = 2018-09-11 - -modification_date = 2020-01-27 - -version = 1.0 - -
-
- -===Hidden Cobra Malware=== - -Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. - -
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- -====Narrative==== -North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ -These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ -In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ -Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. - -====Providing Technologies==== -* Splunk Stream -* Bro -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Network_Resolution -* Network_Traffic -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity -* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf - -creation_date = 2018-06-14 - -modification_date = 2020-01-22 - -version = 2.0 - -
-
- -===Orangeworm Attack Group=== - -Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. - -
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- -====Narrative==== -In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ -Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ -Although the group's motivation is unknown, its goal may be stealing patient information to sell on the black market. Another possible explanation is corporate espionage. \ -Healthcare may be a promising target, because it is notoriously behind in technology, often using older operating systems and neglecting to patch computers. Even so, the group was able to evade detection for a full three years. Sources say that the malware spread quickly within the target networks, infecting computers used to control medical devices, such as MRI and X-ray machines.\ -This Analytic Story is designed to help you detect and investigate suspicious activities that may be indicative of an Orangeworm attack. One detection search looks for command-line arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised. - -====Providing Technologies==== -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia -* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ - -creation_date = 2018-06-14 - -modification_date = 2020-01-22 - -version = 2.0 - -
-
- -===Ransomware=== - -Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. - -
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- -====Narrative==== -Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Palo Alto Firewall -* Bro -* Splunk Stream -* Microsoft Windows - -====Data Models==== -* Endpoint -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ -* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html - -creation_date = 2017-07-24 - -modification_date = 2020-02-04 - -version = 1.1 - -
-
- -===SamSam Ransomware=== - -Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. - -
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- -====Narrative==== -The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ -Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ -SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ -In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ -According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ -This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Splunk Stream -* Palo Alto Firewall -* Apache -* Bro - -====Data Models==== -* Endpoint -* Web -* Network_Traffic - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ -* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ -* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html - -creation_date = 2018-12-13 - -modification_date = 2018-12-13 - -version = 1.0 - -
-
- -===Unusual Processes=== - -Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. - -
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- -====Narrative==== -Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ -This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ -In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html -* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf -* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 - -creation_date = 2016-08-09 - -modification_date = 2020-02-04 - -version = 2.1 - -
-
- -===Windows File Extension and Association Abuse=== - -Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. - -
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- -====Narrative==== -Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ - Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ - Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ -Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ -Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ -* https://attack.mitre.org/wiki/Technique/T1042 - -creation_date = 2018-01-26 - -modification_date = 2018-01-26 - -version = 1.0 - -
-
- -===Windows Service Abuse=== - -Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. - -
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- -====Narrative==== -The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. - -====Providing Technologies==== -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://attack.mitre.org/wiki/Technique/T1050 -* https://attack.mitre.org/wiki/Technique/T1031 - -creation_date = 2017-11-02 - -modification_date = 2017-11-02 - -version = 3.0 +version = 1
@@ -2362,49 +306,43 @@ A common adversary goal is to identify and exfiltrate data of value from a targe Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ Use the searches to detect and monitor suspicious behavior related to these activities. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Bro -* Splunk Stream +====Detections==== +* Email files written outside of the Outlook directory +* Email servers sending high volume traffic to hosts +* Hosts receiving high volume of network traffic from email server +* Suspicious writes to System Volume Information +* Suspicious writes to windows Recycle Bin ====Data Models==== * Endpoint * Network_Traffic -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1043 +* T1074 +* T1114 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== -* CIS 3 +* CIS 7 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.PT ====References==== * https://attack.mitre.org/wiki/Collection * https://attack.mitre.org/wiki/Technique/T1074 -creation_date = 2018-01-08 +date = 2020-02-03 -modification_date = 2020-02-03 - -version = 1.1 +version = 1 @@ -2420,46 +358,62 @@ Detect and investigate tactics, techniques, and procedures leveraged by attacker Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. -====Providing Technologies==== -* Splunk Stream -* Bro -* Palo Alto Firewall -* AWS +====Detections==== +* Clients Connecting to Multiple DNS Servers +* DNS Query Length Outliers - MLTK +* DNS Query Length With High Standard Deviation +* DNS Query Requests Resolved by Unauthorized DNS Servers +* Detect Large Outbound ICMP Packets +* Detect Long DNS TXT Record Response +* Detect Spike in blocked Outbound Traffic from your AWS +* Detect hosts connecting to dynamic domain providers +* Detection of DNS Tunnels +* Excessive DNS Failures +* Prohibited Network Traffic Allowed +* Protocol or Port Mismatch +* TOR Traffic ====Data Models==== * Network_Resolution * Network_Traffic -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1043 +* T1048 +* T1095 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control +* Delivery =====CIS===== +* CIS 1 +* CIS 11 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 +* CIS 9 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* ID.AM +* PR.AC +* PR.DS +* PR.IP +* PR.PT ====References==== * https://attack.mitre.org/wiki/Command_and_Control * https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware -creation_date = 2018-06-01 +date = 2018-06-01 -modification_date = 2018-06-01 - -version = 1.0 +version = 1 @@ -2475,33 +429,24 @@ Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2. -====Providing Technologies==== -* Splunk Stream -* Bro +====Detections==== +* Detect DNS requests to Phishing Sites leveraging EvilGinx2 ====Data Models==== * Network_Resolution -* Web -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1192 =====Kill Chain Phases===== * Command and Control =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 7 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE * DE.CM ====References==== @@ -2509,11 +454,9 @@ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilG * https://attack.mitre.org/techniques/T1192/ * https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ -creation_date = 2019-04-29 +date = 2019-04-29 -modification_date = 2019-04-29 - -version = 1.0 +version = 1 @@ -2530,47 +473,112 @@ Credential dumping—gathering credentials from a target system, often hashed Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ The detection searches in this Analytic Story monitor access to the Local Security Authority Subsystem Service (LSASS) process, the usage of shadowcopies for credential dumping and some other techniques for credential dumping. -====Providing Technologies==== -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Access LSASS Memory for Dump Creation +* Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +* Attempted Credential Dump From Registry via Reg exe +* Create Remote Thread into LSASS +* Creation of Shadow Copy +* Creation of Shadow Copy with wmic and powershell +* Credential Dumping via Copy Command from Shadow Copy +* Credential Dumping via Symlink to Shadow Copy +* Detect Credential Dumping through LSASS access +* Detect Mimikatz Using Loaded Images +* Dump LSASS via comsvcs DLL +* Unsigned Image Loaded by LSASS ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1003 +* T1064 +* T1086 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives +* Installation =====CIS===== +* CIS 16 * CIS 3 +* CIS 5 +* CIS 6 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.AC +* PR.IP ====References==== * https://attack.mitre.org/wiki/Technique/T1003 * https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html -creation_date = 2018-08-08 +date = 2020-02-04 -modification_date = 2020-02-04 +version = 3 -version = 3.0 + + + +===Disabling Security Tools=== + +Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. + +
+
+ +====Narrative==== +Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). + +====Detections==== +* Attempt To Add Certificate To Untrusted Store +* Attempt To Stop Security Service +* Processes launching netsh +* Sc exe Manipulating Windows Services +* Suspicious Reg exe Process +* Unload Sysmon Filter Driver + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1031 +* T1050 +* T1059 +* T1089 +* T1112 + +=====Kill Chain Phases===== +* Actions on Objectives +* Installation + +=====CIS===== +* CIS 3 +* CIS 5 +* CIS 8 + +=====NIST===== +* DE.CM +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +====References==== +* https://attack.mitre.org/wiki/Technique/T1089 +* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf + +date = 2020-02-04 + +version = 2
@@ -2593,33 +601,38 @@ On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.\ The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment. -====Providing Technologies==== -* Splunk Stream -* Bro +====Detections==== +* Clients Connecting to Multiple DNS Servers +* DNS Query Requests Resolved by Unauthorized DNS Servers +* DNS record changed +* Detect hosts connecting to dynamic domain providers ====Data Models==== * Network_Resolution -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1048 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control =====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* ID.AM +* PR.DS +* PR.IP +* PR.PT ====References==== * https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html @@ -2627,66 +640,9 @@ The searches in this Analytic Story help you detect and investigate activities t * http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ * https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html -creation_date = 2017-11-21 +date = 2020-02-04 -modification_date = 2020-02-04 - -version = 1.0 - - - - -===Disabling Security Tools=== - -Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. - -
-
- -====Narrative==== -Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). - -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://attack.mitre.org/wiki/Technique/T1089 -* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ -* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf - -creation_date = 2018-04-09 - -modification_date = 2020-02-04 - -version = 2.0 +version = 1
@@ -2705,50 +661,42 @@ An adversary can use lateral movement for multiple purposes, including remote ex If there is evidence of lateral movement, it is imperative for analysts to collect evidence of the associated offending hosts. For example, an attacker might leverage host A to gain access to host B. From there, the attacker may try to move laterally to host C. In this example, the analyst should gather as much information as possible from all three hosts. \ It is also important to collect authentication logs for each host, to ensure that the offending accounts are well-documented. Analysts should account for all processes to ensure that the attackers did not install unauthorized software. -====Providing Technologies==== -* Microsoft Windows -* Bro -* Splunk Stream -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Detect Activity Related to Pass the Hash Attacks +* Remote Desktop Network Traffic +* Remote Desktop Process Running On System +* Schtasks scheduling job on remote system ====Data Models==== -* Network_Traffic * Endpoint +* Network_Traffic -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1053 +* T1075 +* T1076 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== +* CIS 16 * CIS 3 -* CIS 8 -* CIS 12 +* CIS 9 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE -* DE.CM +* PR.AC +* PR.IP ====References==== -* https://www.binarydefense.com/reliably-detecting-pass-the-hash/ * https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html -creation_date = 2016-09-13 +date = 2020-02-04 -modification_date = 2020-02-04 - -version = 2.0 +version = 2 @@ -2772,46 +720,43 @@ Often, a simple inspection of the process name and path can tell you if the syst It can also be very helpful to examine various behaviors of the process of interest or the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might be worth further scrutiny. If a process is suspect, a review of the network connections made in and around the time of the event and/or whether the process spawned any child processes could be helpful, as well.\ In the event a system is suspected of having been compromised via a malicious website, we suggest reviewing the browsing activity from that system around the time of the event. If categories are given for the URLs visited, that can help you zero in on possible malicious sites. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +* Malicious PowerShell Process - Connect To Internet With Hidden Window +* Malicious PowerShell Process - Encoded Command +* Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +* Malicious PowerShell Process With Obfuscation Techniques ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1064 +* T1086 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control =====CIS===== * CIS 3 +* CIS 7 * CIS 8 -* CIS 12 =====NIST===== +* DE.CM * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM ====References==== * https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ * https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ -creation_date = 2016-09-18 +date = 2017-08-23 -modification_date = 2017-08-23 - -version = 4.0 +version = 4 @@ -2833,45 +778,35 @@ Following is a typical series of events, according to an [article by Trend Micro 1. Powershell executes a reverse shell, rendering the exploit successful As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ This Analytic Story focuses on detecting signs that a malicious payload has been injected into your environment. For example, one search detects outlook.exe writing a .zip file. Another looks for suspicious .lnk files launching processes. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Detect Oulook exe writing a zip file +* Suspicious LNK file launching a process ====Data Models==== -* Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1193 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives +* Installation =====CIS===== -* CIS 3 +* CIS 7 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM +* ID.AM +* PR.DS ====References==== * https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html -creation_date = 2019-04-29 +date = 2019-04-29 -modification_date = 2019-04-29 - -version = 1.0 +version = 1 @@ -2913,46 +848,47 @@ If behavioral searches included in this story yield positive hits, iDefense reco 1. b8ec65dab97cdef3cd256cc4753f0c54\ 1. 04d83cd3813698de28cfbba326d7647c -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* First time seen command line argument +* Malicious PowerShell Process - Connect To Internet With Hidden Window +* Registry Keys Used For Persistence +* Unusually Long Command Line +* Unusually Long Command Line - MLTK ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1059 +* T1064 +* T1086 +* T1103 +* T1131 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control =====CIS===== * CIS 3 +* CIS 7 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.IP +* PR.PT ====References==== * https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ * http://blog.amossys.fr/badflick-is-not-so-bad.html -creation_date = 2018-07-24 +date = 2020-01-22 -modification_date = 2020-01-22 - -version = 1.0 +version = 1 @@ -2968,43 +904,33 @@ Use the searches in this Analytic Story to help you detect structured query lang It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. -====Providing Technologies==== -* Splunk Stream -* Bro +====Detections==== +* SQL Injection with Long URLs ====Data Models==== * Web -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1043 =====Kill Chain Phases===== -* Command and Control +* Delivery =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 18 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE * DE.CM ====References==== * https://capec.mitre.org/data/definitions/66.html * https://www.incapsula.com/web-application-security/sql-injection.html -creation_date = 2016-09-13 +date = 2017-09-19 -modification_date = 2017-09-19 - -version = 1.0 +version = 1 @@ -3019,47 +945,46 @@ Leveraging the Windows command-line interface (CLI) is one of the most common at ====Narrative==== The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Detect Prohibited Applications Spawning cmd exe +* Detect Use of cmd exe to Launch Script Interpreters +* First time seen command line argument +* System Processes Run From Unexpected Locations +* Unusually Long Command Line +* Unusually Long Command Line - MLTK ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1036 +* T1059 +* T1064 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control +* Exploitation =====CIS===== * CIS 3 * CIS 8 -* CIS 12 =====NIST===== +* DE.CM * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM ====References==== * https://attack.mitre.org/wiki/Technique/T1059 * https://www.microsoft.com/en-us/wdsi/threats/macro-malware * https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf -creation_date = 2017-10-09 +date = 2020-02-03 -modification_date = 2020-02-03 - -version = 2.1 +version = 2 @@ -3074,44 +999,53 @@ Attackers often attempt to hide within or otherwise abuse the domain name system ====Narrative==== Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. -====Providing Technologies==== -* Splunk Stream -* Bro +====Detections==== +* Clients Connecting to Multiple DNS Servers +* DNS Query Length Outliers - MLTK +* DNS Query Length With High Standard Deviation +* DNS Query Requests Resolved by Unauthorized DNS Servers +* Detect Long DNS TXT Record Response +* Detect hosts connecting to dynamic domain providers +* Detection of DNS Tunnels +* Excessive DNS Failures ====Data Models==== * Network_Resolution -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1043 +* T1048 =====Kill Chain Phases===== +* Actions on Objectives * Command and Control =====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 +* CIS 9 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* ID.AM +* PR.DS +* PR.IP +* PR.PT ====References==== * http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ * http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680 * https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 -creation_date = 2016-09-13 +date = 2017-09-18 -modification_date = 2017-09-18 - -version = 1.0 +version = 1 @@ -3130,44 +1064,38 @@ Once a phishing message has been detected, the next steps are to answer the foll 1. When did the targeted campaign begin?\ 1. Have any users interacted with the content of the messages (by downloading an attachment or clicking on a malicious URL)?This Analytic Story provides detection searches to identify suspicious emails, as well as contextual and investigative searches to help answer some of these questions. -====Providing Technologies==== -* Microsoft Exchange -* Bro -* Splunk Stream +====Detections==== +* Email Attachments With Lots Of Spaces +* Monitor Email For Brand Abuse +* Suspicious Email - UBA Anomaly +* Suspicious Email Attachment Extensions ====Data Models==== * Email * UEBA -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration =====Kill Chain Phases===== -* Command and Control +* Delivery =====CIS===== -* CIS 3 -* CIS 8 * CIS 12 +* CIS 3 +* CIS 7 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE -* DE.CM +* PR.IP ====References==== * https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ -creation_date = 2017-03-24 +date = 2020-01-27 -modification_date = 2020-01-27 - -version = 1.0 +version = 1 @@ -3184,47 +1112,41 @@ One common adversary tactic is to bypass application white-listing solutions via One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ The searches in this story help you detect and investigate suspicious activity that may indicate that an attacker is leveraging mshta.exe to execute malicious code. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Detect Prohibited Applications Spawning cmd exe +* Detect mshta exe running scripts in command-line arguments +* Registry Keys Used For Persistence ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1059 +* T1103 +* T1131 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives +* Exploitation =====CIS===== -* CIS 3 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.PT ====References==== * https://redcanary.com/blog/windows-registry-attacks-threat-detection/ * https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5 * https://attack.mitre.org/wiki/Technique/T1170 -creation_date = 2018-08-07 +date = 2020-02-03 -modification_date = 2020-02-03 - -version = 1.1 +version = 1 @@ -3241,30 +1163,25 @@ Okta is the leading single sign on (SSO) provider, allowing users to authenticat While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ With people moving quickly to adopt web-based applications and ways to manage them, many are still struggling to understand how best to monitor these environments. This analytic story provides searches to help monitor this environment, and identify events and activity that warrant further investigation such as credential stuffing or password spraying attacks, and users logging in from multiple locations when travel is disallowed. -====Providing Technologies==== -* Okta +====Detections==== +* Multiple Okta Users With Invalid Credentails From The Same IP +* Okta Account Lockout Events +* Okta Failed SSO Attempts +* Okta User Logins From Multiple Cities ====Data Models==== -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1078 =====Kill Chain Phases===== -* Command and Control =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 16 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE * DE.CM ====References==== @@ -3272,68 +1189,9 @@ With people moving quickly to adopt web-based applications and ways to manage th * https://owasp.org/www-community/attacks/Credential_stuffing * https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work -creation_date = 2020-04-02 +date = 2020-04-02 -modification_date = 2020-04-02 - -version = 1.0 - - - - -===Suspicious WMI Use=== - -Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. - -
-
- -====Narrative==== -WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ -The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ -In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end. - -====Providing Technologies==== -* Carbon Black Response -* Sysmon -* Tanium -* Ziften -* CrowdStrike Falcon -* Microsoft Windows - -====Data Models==== -* Endpoint - -====Mappings==== - -=====ATT&CK===== -* Command and Control -* Exfiltration - -=====Kill Chain Phases===== -* Command and Control - -=====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 - -=====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -====References==== -* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf -* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html - -creation_date = 2017-01-13 - -modification_date = 2018-10-23 - -version = 2.0 +version = 1
@@ -3350,47 +1208,105 @@ Attackers are developing increasingly sophisticated techniques for hijacking tar The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Disabling Remote User Account Control +* Monitor Registry Keys for Print Monitors +* Reg exe used to hide files directories via registry keys +* Registry Keys Used For Persistence +* Registry Keys Used For Privilege Escalation +* Registry Keys for Creating SHIM Databases +* Remote Registry Key modifications +* Suspicious Changes to File Associations ====Data Models==== * Endpoint -* Change_Analysis -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1015 +* T1042 +* T1103 +* T1112 +* T1131 +* T1138 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== * CIS 3 +* CIS 5 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.AC +* PR.IP +* PR.PT ====References==== * https://redcanary.com/blog/windows-registry-attacks-threat-detection/ * https://attack.mitre.org/wiki/Technique/T1112 -creation_date = 2018-05-31 +date = 2018-05-31 -modification_date = 2018-05-31 +version = 1 -version = 1.0 + + + +===Suspicious WMI Use=== + +Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. + +
+
+ +====Narrative==== +WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ +The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ +In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end. + +====Detections==== +* Process Execution via WMI +* Remote Process Instantiation via WMI +* Remote WMI Command Attempt +* Script Execution via WMI +* WMI Permanent Event Subscription +* WMI Permanent Event Subscription - Sysmon +* WMI Temporary Event Subscription + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1047 +* T1084 + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 3 +* CIS 5 + +=====NIST===== +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +====References==== +* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf +* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html + +date = 2018-10-23 + +version = 2
@@ -3405,45 +1321,38 @@ Detect tactics used by malware to evade defenses on Windows endpoints. A few of ====Narrative==== Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Disabling Remote User Account Control +* Hiding Files And Directories With Attrib exe +* Reg exe used to hide files directories via registry keys +* Remote Registry Key modifications +* Suspicious Reg exe Process ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1089 +* T1112 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== -* CIS 3 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC -* DE.AE * DE.CM +* PR.PT ====References==== * https://attack.mitre.org/wiki/Defense_Evasion -creation_date = 2017-10-11 +date = 2018-05-31 -modification_date = 2018-05-31 - -version = 1.0 +version = 1 @@ -3459,48 +1368,47 @@ Adversaries often try to cover their tracks by manipulating Windows logs. Use th Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Windows +====Detections==== +* Deleting Shadow Copies +* Suspicious wevtutil Usage +* USN Journal Deletion +* Windows Event Log Cleared ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1070 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives =====CIS===== +* CIS 10 * CIS 3 +* CIS 5 +* CIS 6 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* DE.DP +* PR.AC +* PR.AT +* PR.IP +* PR.PT ====References==== * https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ * https://zeltser.com/security-incident-log-review-checklist/ * http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html -creation_date = 2017-02-17 +date = 2017-09-12 -modification_date = 2017-09-12 - -version = 2.0 +version = 2 @@ -3515,37 +1423,50 @@ Monitor for activities and techniques associated with maintaining persistence on ====Narrative==== Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Tanium -* Ziften -* Sysmon +====Detections==== +* Detect Path Interception By Creation Of program exe +* Hiding Files And Directories With Attrib exe +* Monitor Registry Keys for Print Monitors +* Reg exe Manipulating Windows Services Registry Keys +* Reg exe used to hide files directories via registry keys +* Registry Keys Used For Persistence +* Registry Keys for Creating SHIM Databases +* Remote Registry Key modifications +* Sc exe Manipulating Windows Services +* Schtasks used for forcing a reboot +* Shim Database File Creation +* Shim Database Installation With Suspicious Parameters ====Data Models==== * Endpoint -* Change_Analysis -====Mappings==== +====Tags==== =====ATT&CK===== -* Command and Control -* Exfiltration +* T1031 +* T1050 +* T1053 +* T1089 +* T1103 +* T1131 +* T1138 =====Kill Chain Phases===== -* Command and Control +* Actions on Objectives +* Installation =====CIS===== * CIS 3 +* CIS 5 * CIS 8 -* CIS 12 =====NIST===== -* PR.IP -* PR.PT -* PR.AC * DE.AE * DE.CM +* PR.AC +* PR.AT +* PR.IP +* PR.PT ====References==== * http://www.fuzzysecurity.com/tutorials/19.html @@ -3554,11 +1475,9 @@ Maintaining persistence is one of the first steps taken by attackers after the i * https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html * https://www.youtube.com/watch?v=dq2Hv7J9fvk -creation_date = 2017-04-19 +date = 2018-05-31 -modification_date = 2018-05-31 - -version = 2.0 +version = 2 @@ -3573,47 +1492,1906 @@ Monitor for and investigate activities that may be associated with a Windows pri ====Narrative==== Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. -====Providing Technologies==== -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +====Detections==== +* Child Processes of Spoolsv exe +* Overwriting Accessibility Binaries +* Registry Keys Used For Privilege Escalation +* Uncommon Processes On Endpoint ====Data Models==== * Endpoint -====Mappings==== +====Tags==== =====ATT&CK===== +* T1015 +* T1068 + +=====Kill Chain Phases===== +* Actions on Objectives +* Exploitation + +=====CIS===== +* CIS 2 +* CIS 8 + +=====NIST===== +* DE.CM +* ID.AM +* PR.DS +* PR.PT + +====References==== +* https://attack.mitre.org/tactics/TA0004/ + +date = 2020-02-04 + +version = 2 + + + + + +==Best Practices== + +===Account Monitoring and Controls=== + +A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. + +
+
+ +====Narrative==== +Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. + +====Detections==== +* Detect Excessive Account Lockouts From Endpoint +* Detect Excessive User Account Lockouts +* Identify New User Accounts +* Short Lived Windows Accounts + +====Data Models==== +* Change + +====Tags==== + +=====ATT&CK===== +* T1078 +* T1136 + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 16 + +=====NIST===== +* PR.IP + +====References==== + +date = 2017-09-06 + +version = 1 + +
+
+ +===Asset Tracking=== + +Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. + +
+
+ +====Narrative==== +This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. + +====Detections==== +* Detect Unauthorized Assets by MAC address + +====Data Models==== +* Network_Sessions + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 1 + +=====NIST===== +* PR.DS + +====References==== +* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ + +date = 2017-09-13 + +version = 1 + +
+
+ +===Monitor Backup Solution=== + +Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. + +
+
+ +====Narrative==== +Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. + +====Detections==== +* Extended Period Without Successful Netbackup Backups +* Unsuccessful Netbackup backups + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 10 + +=====NIST===== +* PR.IP + +====References==== +* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ + +date = 2017-09-12 + +version = 1 + +
+
+ +===Monitor for Unauthorized Software=== + +Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. + +
+
+ +====Narrative==== +It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ +It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. + +====Detections==== +* Prohibited Software On Endpoint + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 2 + +=====NIST===== +* PR.DS + +====References==== +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + +date = 2017-09-15 + +version = 1 + +
+
+ +===Monitor for Updates=== + +Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. + +
+
+ +====Narrative==== +It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ +Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ +Microsoft releases updates for Windows systems on a monthly cadence. They should be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed. + +====Detections==== +* No Windows Updates in a time frame + +====Data Models==== +* Updates + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 18 + +=====NIST===== +* PR.MA + +====References==== +* https://learn.cisecurity.org/20-controls-download + +date = 2017-09-15 + +version = 1 + +
+
+ +===Prohibited Traffic Allowed or Protocol Mismatch=== + +Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. + +
+
+ +====Narrative==== +A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. + +====Detections==== +* Detect hosts connecting to dynamic domain providers +* Prohibited Network Traffic Allowed +* Protocol or Port Mismatch +* TOR Traffic + +====Data Models==== +* Network_Resolution +* Network_Traffic + +====Tags==== + +=====ATT&CK===== +* T1043 +* T1048 + +=====Kill Chain Phases===== +* Actions on Objectives * Command and Control -* Exfiltration +* Delivery + +=====CIS===== +* CIS 12 +* CIS 13 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* PR.AC + +====References==== +* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ + +date = 2017-09-11 + +version = 1 + +
+
+ +===Router and Infrastructure Security=== + +Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. + +
+
+ +====Narrative==== +Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ +This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. + +====Detections==== +* Detect New Login Attempts to Routers + +====Data Models==== +* Authentication + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 11 + +=====NIST===== +* PR.IP + +====References==== +* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html +* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html + +date = 2017-09-12 + +version = 1 + +
+
+ +===Use of Cleartext Protocols=== + +Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. + +
+
+ +====Narrative==== +Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. + +====Detections==== +* Protocols passing authentication in cleartext + +====Data Models==== +* Network_Traffic + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 14 + +=====NIST===== +* PR.DS + +====References==== +* https://www.monkey.org/~dugsong/dsniff/ + +date = 2017-09-15 + +version = 1 + +
+
+ + +==Cloud Security== + +===AWS Cross Account Activity=== + +Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +
+
+ +====Narrative==== +Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ +Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ +This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. + +====Detections==== +* AWS Cross Account Activity From Previously Unseen Account + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 16 + +=====NIST===== +* DE.AE + +====References==== +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + +date = 2018-06-04 + +version = 1 + +
+
+ +===AWS Cryptomining=== + +Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. + +
+
+ +====Narrative==== +Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +hen malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So, it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ +This Analytic Story is focused on detecting suspicious new instances in your EC2 environment to help prevent such a disaster. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. + +====Detections==== +* Abnormally High AWS Instances Launched by User +* EC2 Instance Started In Previously Unseen Region +* EC2 Instance Started With Previously Unseen AMI +* EC2 Instance Started With Previously Unseen Instance Type +* EC2 Instance Started With Previously Unseen User + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 + +=====NIST===== +* DE.AE +* DE.DP +* ID.AM + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +date = 2018-03-08 + +version = 1 + +
+
+ +===AWS Network ACL Activity=== + +Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. + +
+
+ +====Narrative==== +AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. + +====Detections==== +* AWS Network Access Control List Created with All Open Ports +* AWS Network Access Control List Deleted +* Detect Spike in Network ACL Activity +* Detect Spike in blocked Outbound Traffic from your AWS + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control + +=====CIS===== +* CIS 11 +* CIS 12 + +=====NIST===== +* DE.AE +* DE.CM +* DE.DP +* PR.AC + +====References==== +* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html +* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ + +date = 2018-05-21 + +version = 2 + +
+
+ +===AWS Suspicious Provisioning Activities=== + +Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. + +
+
+ +====Narrative==== +Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ +This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. + +====Detections==== +* AWS Cloud Provisioning From Previously Unseen City +* AWS Cloud Provisioning From Previously Unseen Country +* AWS Cloud Provisioning From Previously Unseen IP Address +* AWS Cloud Provisioning From Previously Unseen Region + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 1 + +=====NIST===== +* ID.AM + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +date = 2018-03-16 + +version = 1 + +
+
+ +===AWS User Monitoring=== + +Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. + +
+
+ +====Narrative==== +It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ +Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ +The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. + +====Detections==== +* Detect API activity from users without MFA +* Detect AWS API Activities From Unapproved Accounts +* Detect Spike in AWS API Activity +* Detect Spike in Security Group Activity +* Detect new API calls from user roles + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 1 +* CIS 16 + +=====NIST===== +* DE.CM +* DE.DP +* ID.AM +* PR.AC + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +* https://redlock.io/blog/cryptojacking-tesla + +date = 2018-03-12 + +version = 1 + +
+
+ +===Cloud Cryptomining=== + +Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. + +
+
+ +====Narrative==== +Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ +This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. + +====Detections==== +* Abnormally High AWS Instances Launched by User - MLTK +* Cloud Compute Instance Created By Previously Unseen User +* Cloud Compute Instance Created With Previously Unseen Image +* Cloud Compute Instance Created With Previously Unseen Instance Type +* Cloud Compute Instance Started In Previously Unused Region + +====Data Models==== +* Cloud_Infrastructure + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 + +=====NIST===== +* DE.AE +* DE.DP +* ID.AM + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +date = 2019-10-02 + +version = 1 + +
+
+ +===Container Implantation Monitoring and Investigation=== + +Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. + +
+
+ +====Narrative==== +Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. + +====Detections==== +* GCP GCR container uploaded +* New container uploaded to AWS ECR + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== + +=====NIST===== + +====References==== +* https://github.com/splunk/cloud-datamodel-security-research + +date = 2020-02-20 + +version = 1 + +
+
+ +===Kubernetes Scanning Activity=== + +This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. + +
+
+ +====Narrative==== +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. + +====Detections==== +* Amazon EKS Kubernetes Pod scan detection +* Amazon EKS Kubernetes cluster scan detection +* GCP Kubernetes cluster scan detection + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Reconnaissance + +=====CIS===== + +=====NIST===== + +====References==== +* https://github.com/splunk/cloud-datamodel-security-research + +date = 2020-04-15 + +version = 1 + +
+
+ +===Suspicious AWS EC2 Activities=== + +Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. + +
+
+ +====Narrative==== +AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. + +====Detections==== +* Abnormally High AWS Instances Launched by User +* Abnormally High AWS Instances Launched by User - MLTK +* Abnormally High AWS Instances Terminated by User +* Abnormally High AWS Instances Terminated by User - MLTK +* EC2 Instance Started In Previously Unseen Region +* EC2 Instance Started With Previously Unseen User + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 1 +* CIS 12 +* CIS 13 + +=====NIST===== +* DE.AE +* DE.DP +* ID.AM + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +date = 2018-02-09 + +version = 1 + +
+
+ +===Suspicious AWS Login Activities=== + +Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. + +
+
+ +====Narrative==== +It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. + +====Detections==== +* Detect AWS Console Login by User from New City +* Detect AWS Console Login by User from New Country +* Detect AWS Console Login by User from New Region +* Detect new user AWS Console Login + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 16 + +=====NIST===== +* DE.AE +* DE.DP + +====References==== +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + +date = 2019-05-01 + +version = 1 + +
+
+ +===Suspicious AWS S3 Activities=== + +Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +
+
+ +====Narrative==== +As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ +Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ +Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. + +====Detections==== +* Detect New Open S3 buckets +* Detect S3 access from a new IP +* Detect Spike in S3 Bucket deletion + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 13 +* CIS 14 + +=====NIST===== +* DE.CM +* DE.DP +* PR.AC +* PR.DS + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ + +date = 2018-07-24 + +version = 2 + +
+
+ +===Suspicious AWS Traffic=== + +Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). + +
+
+ +====Narrative==== +A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ +Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ + Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ +The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. + +====Detections==== +* Detect Spike in blocked Outbound Traffic from your AWS + +====Data Models==== + +====Tags==== + +=====ATT&CK===== =====Kill Chain Phases===== * Command and Control =====CIS===== -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 11 =====NIST===== +* PR.AC + +====References==== +* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ + +date = 2018-05-07 + +version = 1 + +
+
+ +===Unusual AWS EC2 Modifications=== + +Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. + +
+
+ +====Narrative==== +A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ + Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. + +====Detections==== +* EC2 Instance Modified With Previously Unseen User + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 1 + +=====NIST===== +* ID.AM + +====References==== +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +date = 2018-04-09 + +version = 1 + +
+
+ + +==Malware== + +===ColdRoot MacOS RAT=== + +Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. + +
+
+ +====Narrative==== +Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ +This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ +Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. + +====Detections==== +* Osquery pack - ColdRoot detection +* Processes Tapping Keyboard Events + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Command and Control + +=====CIS===== +* CIS 4 +* CIS 8 + +=====NIST===== +* DE.DP +* PR.PT + +====References==== +* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ +* https://objective-see.com/blog/blog_0x2A.html +* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ + +date = 2019-01-09 + +version = 1 + +
+
+ +===DHS Report TA18-074A=== + +Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. + +
+
+ +====Narrative==== +The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ +There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ +One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ +Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. + +====Detections==== +* Create local admin accounts using net exe +* Detect New Local Admin account +* Detect Outbound SMB Traffic +* Detect PsExec With accepteula Flag +* First time seen command line argument +* Malicious PowerShell Process - Execution Policy Bypass +* Processes launching netsh +* Registry Keys Used For Persistence +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Sc exe Manipulating Windows Services +* Scheduled Task Name Used by Dragonfly Threat Actors +* Single Letter Process On Endpoint +* Suspicious Reg exe Process + +====Data Models==== +* Endpoint +* Network_Traffic + +====Tags==== + +=====ATT&CK===== +* T1031 +* T1043 +* T1050 +* T1053 +* T1059 +* T1064 +* T1078 +* T1086 +* T1089 +* T1103 +* T1112 +* T1131 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Installation + +=====CIS===== +* CIS 12 +* CIS 16 +* CIS 2 +* CIS 3 +* CIS 5 +* CIS 7 +* CIS 8 + +=====NIST===== +* DE.AE +* DE.CM +* ID.AM +* PR.AC +* PR.AT +* PR.DS * PR.IP * PR.PT -* PR.AC + +====References==== +* https://www.us-cert.gov/ncas/alerts/TA18-074A + +date = 2020-01-22 + +version = 2 + +
+
+ +===Dynamic DNS=== + +Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. + +
+
+ +====Narrative==== +Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. + +====Detections==== +* Detect hosts connecting to dynamic domain providers +* Detect web traffic to dynamic domain providers + +====Data Models==== +* Network_Resolution +* Web + +====Tags==== + +=====ATT&CK===== +* T1041 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control + +=====CIS===== +* CIS 13 +* CIS 7 +* CIS 8 + +=====NIST===== +* DE.CM +* DE.DP +* PR.IP + +====References==== +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + +date = 2018-09-06 + +version = 2 + +
+
+ +===Emotet Malware DHS Report TA18-201A === + +Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. + +
+
+ +====Narrative==== +The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ +According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ +The searches in this Analytic Story will help you find executables that are rarely used in your environment, specific registry paths that malware often uses to ensure survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that Emotet or other malware has compromised your environment. + +====Detections==== +* Detect Rare Executables +* Detect Use of cmd exe to Launch Script Interpreters +* Detection of tools built by NirSoft +* Email Attachments With Lots Of Spaces +* Prohibited Software On Endpoint +* Registry Keys Used For Persistence +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Suspicious Email Attachment Extensions + +====Data Models==== +* Email +* Endpoint +* Network_Traffic + +====Tags==== + +=====ATT&CK===== +* T1043 +* T1059 +* T1072 +* T1087 +* T1103 +* T1131 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Delivery +* Exploitation +* Installation + +=====CIS===== +* CIS 12 +* CIS 2 +* CIS 3 +* CIS 7 +* CIS 8 + +=====NIST===== * DE.AE * DE.CM +* ID.AM +* PR.DS +* PR.IP +* PR.PT + +====References==== +* https://www.us-cert.gov/ncas/alerts/TA18-201A +* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf +* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html + +date = 2020-01-27 + +version = 1 + +
+
+ +===Hidden Cobra Malware=== + +Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. + +
+
+ +====Narrative==== +North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ +These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ +In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ +Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. + +====Detections==== +* Create or delete windows shares using net exe +* DNS Query Length Outliers - MLTK +* DNS Query Length With High Standard Deviation +* Detect Outbound SMB Traffic +* First time seen command line argument +* Remote Desktop Network Traffic +* Remote Desktop Process Running On System +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Suspicious File Write + +====Data Models==== +* Endpoint +* Network_Resolution +* Network_Traffic + +====Tags==== + +=====ATT&CK===== +* T1043 +* T1059 +* T1064 +* T1076 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control + +=====CIS===== +* CIS 12 +* CIS 16 +* CIS 3 +* CIS 8 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* PR.AC +* PR.IP +* PR.PT + +====References==== +* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf + +date = 2020-01-22 + +version = 2 + +
+
+ +===Orangeworm Attack Group=== + +Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. + +
+
+ +====Narrative==== +In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ +Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ +Although the group's motivation is unknown, its goal may be stealing patient information to sell on the black market. Another possible explanation is corporate espionage. \ +Healthcare may be a promising target, because it is notoriously behind in technology, often using older operating systems and neglecting to patch computers. Even so, the group was able to evade detection for a full three years. Sources say that the malware spread quickly within the target networks, infecting computers used to control medical devices, such as MRI and X-ray machines.\ +This Analytic Story is designed to help you detect and investigate suspicious activities that may be indicative of an Orangeworm attack. One detection search looks for command-line arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised. + +====Detections==== +* First Time Seen Running Windows Service +* First time seen command line argument +* Sc exe Manipulating Windows Services + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1031 +* T1050 +* T1059 +* T1089 + +=====Kill Chain Phases===== +* Actions on Objectives +* Installation + +=====CIS===== +* CIS 2 +* CIS 3 +* CIS 5 +* CIS 8 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* ID.AM +* PR.AC +* PR.AT +* PR.DS +* PR.IP +* PR.PT + +====References==== +* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia +* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ + +date = 2020-01-22 + +version = 2 + +
+
+ +===Ransomware=== + +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. + +
+
+ +====Narrative==== +Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. + +====Detections==== +* Common Ransomware Extensions +* Common Ransomware Notes +* Deleting Shadow Copies +* Prohibited Network Traffic Allowed +* Registry Keys Used For Persistence +* Remote Process Instantiation via WMI +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Scheduled tasks used in BadRabbit ransomware +* Schtasks used for forcing a reboot +* Spike in File Writes +* Suspicious wevtutil Usage +* System Processes Run From Unexpected Locations +* TOR Traffic +* USN Journal Deletion +* Unusually Long Command Line +* Unusually Long Command Line - MLTK +* Windows Event Log Cleared + +====Data Models==== +* Endpoint +* Network_Traffic + +====Tags==== + +=====ATT&CK===== +* T1036 +* T1043 +* T1047 +* T1048 +* T1053 +* T1070 +* T1103 +* T1131 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Delivery + +=====CIS===== +* CIS 10 +* CIS 12 +* CIS 3 +* CIS 5 +* CIS 6 +* CIS 8 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* DE.DP +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +====References==== +* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ +* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html + +date = 2020-02-04 + +version = 1 + +
+
+ +===SamSam Ransomware=== + +Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. + +
+
+ +====Narrative==== +The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ +Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ +SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ +In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ +According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ +This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. + +====Detections==== +* Batch File Write to System32 +* Common Ransomware Extensions +* Common Ransomware Notes +* Deleting Shadow Copies +* Detect PsExec With accepteula Flag +* Detect attackers scanning for vulnerable JBoss servers +* Detect malicious requests to exploit JBoss servers +* File with Samsam Extension +* Prohibited Software On Endpoint +* Remote Desktop Network Bruteforce +* Remote Desktop Network Traffic +* Samsam Test File Write +* Spike in File Writes + +====Data Models==== +* Endpoint +* Network_Traffic +* Web + +====Tags==== + +=====ATT&CK===== +* T1059 +* T1076 +* T1082 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Delivery +* Installation +* Reconnaissance + +=====CIS===== +* CIS 10 +* CIS 12 +* CIS 16 +* CIS 18 +* CIS 2 +* CIS 3 +* CIS 4 +* CIS 8 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* ID.AM +* ID.RA +* PR.AC +* PR.DS +* PR.IP +* PR.MA +* PR.PT + +====References==== +* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ +* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ +* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html + +date = 2018-12-13 + +version = 1 + +
+
+ +===Unusual Processes=== + +Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. + +
+
+ +====Narrative==== +Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ +This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ +In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. + +====Detections==== +* Detect Rare Executables +* Detect processes used for System Network Configuration Discovery +* RunDLL Loading DLL By Ordinal +* System Processes Run From Unexpected Locations +* Uncommon Processes On Endpoint +* Unusually Long Command Line +* Unusually Long Command Line - MLTK + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1015 +* T1036 +* T1085 + +=====Kill Chain Phases===== +* Actions on Objectives +* Command and Control +* Installation + +=====CIS===== +* CIS 2 +* CIS 8 + +=====NIST===== +* DE.CM +* ID.AM +* PR.DS +* PR.PT + +====References==== +* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html +* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf +* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 + +date = 2020-02-04 + +version = 2 + +
+
+ +===Windows File Extension and Association Abuse=== + +Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. + +
+
+ +====Narrative==== +Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ + Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ + Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ +Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ +Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. + +====Detections==== +* Execution of File With Spaces Before Extension +* Execution of File with Multiple Extensions +* Suspicious Changes to File Associations + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1042 + +=====Kill Chain Phases===== +* Actions on Objectives + +=====CIS===== +* CIS 3 +* CIS 8 + +=====NIST===== +* DE.CM +* PR.IP +* PR.PT + +====References==== +* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ +* https://attack.mitre.org/wiki/Technique/T1042 + +date = 2018-01-26 + +version = 1 + +
+
+ +===Windows Service Abuse=== + +Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. + +
+
+ +====Narrative==== +The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. + +====Detections==== +* First Time Seen Running Windows Service +* Reg exe Manipulating Windows Services Registry Keys +* Sc exe Manipulating Windows Services + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1031 +* T1050 +* T1089 + +=====Kill Chain Phases===== +* Actions on Objectives +* Installation + +=====CIS===== +* CIS 3 +* CIS 5 +* CIS 8 +* CIS 9 + +=====NIST===== +* DE.AE +* DE.CM +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +====References==== +* https://attack.mitre.org/wiki/Technique/T1050 +* https://attack.mitre.org/wiki/Technique/T1031 + +date = 2017-11-02 + +version = 3 + +
+
+ + +==Vulnerability== + +===Apache Struts Vulnerability=== + +Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. + +
+
+ +====Narrative==== +In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ +The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ +This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ +The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ +First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ +hen looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ +Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ +Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ +Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ +hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ +hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ +In the event that a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that have the file open, what processes created and/or modified the file, and the number of systems that may have this file can help to determine if the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes quickly help determine whether it is malicious in nature.\ +Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, there may be activity initiated via a compromised website the user visited.\ +It can also be very helpful to examine various behaviors of the process of interest or the parent of the process that is of interest. For example, if it turns out that the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might also be worth further scrutiny. If a process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. + +====Detections==== +* Suspicious Java Classes +* Unusually Long Content-Type Length +* Web Servers Executing Suspicious Processes + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1082 + +=====Kill Chain Phases===== +* Actions on Objectives +* Delivery +* Exploitation + +=====CIS===== +* CIS 12 +* CIS 18 +* CIS 3 +* CIS 4 + +=====NIST===== +* DE.AE +* DE.CM +* ID.RA +* PR.IP +* PR.MA +* PR.PT +* RS.MI + +====References==== +* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf + +date = 2018-12-06 + +version = 1 + +
+
+ +===JBoss Vulnerability=== + +In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. + +
+
+ +====Narrative==== +This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ +It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ +hen looking at the target of the behavior uncovered by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to identify other recent events involving the target. This can help tie different events together and give further situational awareness regarding the target host.\ +Various types of information for external systems should be reviewed and, potentially, collected if the incident is, indeed, judged to be malicious. This data may be useful for generating your own threat intelligence, so you can create future alerts.\ +The following factors may assist you in determining whether the event is malicious: \ +1. Country of origin\ +1. Responsible party\ +1. Fully qualified domain names associated with the external IP address\ +1. Registration of fully qualified domain names associated with external IP address Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you qualify and understand the event and possible motivation for the attack. In addition, there are various sources that may provide reputation information on the IP address or domain name, which can assist you in determining whether the event is malicious in nature. Finally, determining whether there are other events associated with the IP address may help connect data points or expose other historic events that might be brought back into scope.\ +Gathering various data on the system of interest can sometimes help quickly determine whether something suspicious is happening. Some of these items include determining who else may have logged into the system recently, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and/or whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ +hen a specific service or application is targeted, it is often helpful to know the associated version, to help determine whether it is vulnerable to a specific exploit.\ +If you suspect an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ +If a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that opened the file, the processes that may have created and/or modified the file, and how many other systems potentially have this file can you determine whether the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes help you quickly determine if it is malicious in nature.\ +Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if svchost.exe is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. \ +It can also be helpful to examine various behaviors of and the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see whether the parent process spawned other processes that might also warrant further scrutiny. If a process is suspect, a review of the network connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. + +====Detections==== +* Detect attackers scanning for vulnerable JBoss servers +* Detect malicious requests to exploit JBoss servers + +====Data Models==== +* Web + +====Tags==== + +=====ATT&CK===== +* T1082 + +=====Kill Chain Phases===== +* Delivery +* Reconnaissance + +=====CIS===== +* CIS 18 + +=====NIST===== +* DE.CM ====References==== -* https://attack.mitre.org/tactics/TA0004/ +* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html -creation_date = 2017-12-07 +date = 2017-09-14 -modification_date = 2020-02-04 - -version = 2.0 +version = 1
-[[Category:V:Lab:drafts]] \ No newline at end of file +===Spectre And Meltdown Vulnerabilities=== + +Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. + +
+
+ +====Narrative==== +Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. + +====Detections==== +* Spectre and Meltdown Vulnerable Systems + +====Data Models==== +* Vulnerabilities + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== + +=====CIS===== +* CIS 4 + +=====NIST===== +* DE.CM + +====References==== +* https://meltdownattack.com/ + +date = 2018-01-08 + +version = 1 + +
+
+ +===Splunk Enterprise Vulnerability=== + +Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. + +
+
+ +====Narrative==== +This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ +\ +1. Splunk Enterprise 6.4.x, prior to 6.4.3\ +1. Splunk Enterprise 6.3.x, prior to 6.3.6\ +1. Splunk Enterprise 6.2.x, prior to 6.2.10\ +1. Splunk Enterprise 6.1.x, prior to 6.1.11\ +1. Splunk Enterprise 6.0.x, prior to 6.0.12\ +1. Splunk Enterprise 5.0.x, prior to 5.0.16\ +1. Splunk Light, prior to 6.4.3CVE-2016-4859 allows attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. (Credit: Noriaki Iwasaki, Cyber Defense Institute, Inc.).\ +It is important to ensure that your Splunk deployment is being kept up to date and is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites. + +====Detections==== +* Open Redirect in Splunk Web + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Delivery + +=====CIS===== +* CIS 18 + +=====NIST===== +* DE.CM + +====References==== +* http://www.splunk.com/view/SP-CAAAPQ6#announce +* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 + +date = 2017-09-19 + +version = 1 + +
+
+ +===Splunk Enterprise Vulnerability CVE-2018-11409=== + +Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. + +
+
+ +====Narrative==== +Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ +Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ +Read more in Splunk's official response: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings.\ +A detection search within this Analytic Story looks for vulnerabilities described in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip. + +====Detections==== +* Splunk Enterprise Information Disclosure + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Delivery + +=====CIS===== +* CIS 18 + +=====NIST===== +* DE.CM + +====References==== +* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 +* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings +* https://www.exploit-db.com/exploits/44865/ + +date = 2018-06-14 + +version = 1 + +
+
+ + diff --git a/docs/stories_categories.md b/docs/stories_categories.md index 8bbbb25361..8e75361535 100644 --- a/docs/stories_categories.md +++ b/docs/stories_categories.md @@ -1,1835 +1,234 @@ + # Categories Analytics stories organized by categories -* [Cloud Security](#cloud-security) +* [Abuse](#Abuse) -* [Best Practices](#best-practices) +* [Adversary Tactics](#Adversary-Tactics) -* [Vulnerability](#vulnerability) +* [Best Practices](#Best-Practices) -* [Abuse](#abuse) +* [Cloud Security](#Cloud-Security) -* [Malware](#malware) +* [Malware](#Malware) -* [Adversary Tactics](#adversary-tactics) - - -## Cloud Security - -* [AWS Cross Account Activity](#aws-cross-account-activity) - -* [AWS Cryptomining](#aws-cryptomining) - -* [AWS Network ACL Activity](#aws-network-acl-activity) - -* [AWS Suspicious Provisioning Activities](#aws-suspicious-provisioning-activities) - -* [AWS User Monitoring](#aws-user-monitoring) - -* [Cloud Cryptomining](#cloud-cryptomining) - -* [Container Implantation Monitoring & Investigation](#container-implantation-monitoring-&-investigation) - -* [Kubernetes Scanning Activity](#kubernetes-scanning-activity) - -* [Suspicious AWS EC2 Activities](#suspicious-aws-ec2-activities) - -* [Suspicious AWS Login Activities](#suspicious-aws-login-activities) - -* [Suspicious AWS S3 Activities](#suspicious-aws-s3-activities) - -* [Suspicious AWS Traffic](#suspicious-aws-traffic) - -* [Unusual AWS EC2 Modifications](#unusual-aws-ec2-modifications) - -### AWS Cross Account Activity -* id = `2f2f610a-d64d-48c2-b57c-967a2b49ab5a` -* creation_date = 2018-06-04 -* modification_date = 2018-06-04 -* version = 1.0 -* spec_version = 2 - -##### Description -Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. - -##### Narrative -Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ -Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ -This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. - -##### Detections -* AWS Cross Account Activity From Previously Unseen Account - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ - -### AWS Cryptomining -* id = `ced74200-8465-4bc3-bd2c-9a782eec6750` -* creation_date = 2018-03-08 -* modification_date = 2018-03-08 -* version = 1.0 -* spec_version = 2 - -##### Description -Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. - -##### Narrative -Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ -hen malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So, it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ -This Analytic Story is focused on detecting suspicious new instances in your EC2 environment to help prevent such a disaster. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. - -##### Detections -* EC2 Instance Started In Previously Unseen Region -* Abnormally High AWS Instances Launched by User -* EC2 Instance Started With Previously Unseen Instance Type -* EC2 Instance Started With Previously Unseen AMI -* EC2 Instance Started With Previously Unseen User - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -### AWS Network ACL Activity -* id = `2e8948a5-5239-406b-b56b-6c50ff268af4` -* creation_date = 2018-01-10 -* modification_date = 2018-05-21 -* version = 2.0 -* spec_version = 2 - -##### Description -Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. - -##### Narrative -AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. - -##### Detections -* AWS Network Access Control List Created with All Open Ports -* AWS Network Access Control List Deleted -* Detect Spike in blocked Outbound Traffic from your AWS -* Detect Spike in Network ACL Activity - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html -* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ - -### AWS Suspicious Provisioning Activities -* id = `3338b567-3804-4261-9889-cf0ca4753c7f` -* creation_date = 2018-03-16 -* modification_date = 2018-03-16 -* version = 1.0 -* spec_version = 2 - -##### Description -Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. - -##### Narrative -Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ -This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. - -##### Detections -* AWS Cloud Provisioning From Previously Unseen Country -* AWS Cloud Provisioning From Previously Unseen Region -* AWS Cloud Provisioning From Previously Unseen City -* AWS Cloud Provisioning From Previously Unseen IP Address - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -### AWS User Monitoring -* id = `2e8948a5-5239-406b-b56b-6c50f1269af3` -* creation_date = 2018-03-12 -* modification_date = 2018-03-12 -* version = 1.0 -* spec_version = 2 - -##### Description -Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. - -##### Narrative -It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ -In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ -Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ -The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. - -##### Detections -* Detect AWS API Activities From Unapproved Accounts -* Detect Spike in AWS API Activity -* Detect new API calls from user roles -* Detect Spike in Security Group Activity -* Detect API activity from users without MFA - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -* https://redlock.io/blog/cryptojacking-tesla - -### Cloud Cryptomining -* id = `3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a` -* creation_date = 2019-10-02 -* modification_date = 2019-10-02 -* version = 1.0 -* spec_version = 2 - -##### Description -Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. - -##### Narrative -Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ -When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ -This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. - -##### Detections -* Cloud Compute Instance Started In Previously Unused Region -* Cloud Compute Instance Created With Previously Unseen Instance Type -* Cloud Compute Instance Created With Previously Unseen Image -* Cloud Compute Instance Created By Previously Unseen User -* Abnormally High AWS Instances Launched by User - MLTK - -##### Providing Technologies -* AWS -* Azure -* GCP - -##### Data Models -Cloud_Infrastructure - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -### Container Implantation Monitoring & Investigation -* id = `aa0e28b1-0521-4b6f-9d2a-7b87e34af246` -* creation_date = 2020-02-20 -* modification_date = 2020-02-20 -* version = 1.0 -* spec_version = 2 - -##### Description -Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. - -##### Narrative -Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. - -##### Detections -* New container uploaded to AWS ECR -* GCP GCR container uploaded - -##### Providing Technologies -* GCP -* AWS - -##### Data Models -Cloud_Infrastructure - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rod Soto, Rico Valdez -* email = rsoto@splunk.com, rvaldez@splunk.com -* company = Splunk - -##### References -* https://github.com/splunk/cloud-datamodel-security-research - -### Kubernetes Scanning Activity -* id = `a9ef59cf-e981-4e66-9eef-bb049f695c09` -* creation_date = 2020-03-24 -* modification_date = 2020-04-15 -* version = 1.0 -* spec_version = 2 - -##### Description -This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. - -##### Narrative -Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. - -##### Detections -* GCP Kubernetes cluster scan detection -* Amazon EKS Kubernetes cluster scan detection -* Amazon EKS Kubernetes Pod scan detection - -##### Providing Technologies -* AWS -* GCP - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rod Soto -* email = rsoto@splunk.com -* company = Splunk - -##### References -* https://github.com/splunk/cloud-datamodel-security-research - -### Suspicious AWS EC2 Activities -* id = `2e8948a5-5239-406b-b56b-6c50f1268af3` -* creation_date = 2018-02-09 -* modification_date = 2018-02-09 -* version = 1.0 -* spec_version = 2 - -##### Description -Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. - -##### Narrative -AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. - -##### Detections -* EC2 Instance Started In Previously Unseen Region -* Abnormally High AWS Instances Terminated by User -* Abnormally High AWS Instances Launched by User -* EC2 Instance Started With Previously Unseen User -* Abnormally High AWS Instances Launched by User - MLTK -* Abnormally High AWS Instances Terminated by User - MLTK - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - -### Suspicious AWS Login Activities -* id = `2e8948a5-5239-406b-b56b-6c59f1268af3` -* creation_date = 2018-02-24 -* modification_date = 2019-05-01 -* version = 1.0 -* spec_version = 2 - -##### Description -Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. - -##### Narrative -It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. - -##### Detections -* Detect new user AWS Console Login -* Detect AWS Console Login by User from New City -* Detect AWS Console Login by User from New Region -* Detect AWS Console Login by User from New Country - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk -* name = Jason Brewer -* email = jbrewer@splunk.com -* company = Splunk - -##### References -* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html - -### Suspicious AWS S3 Activities -* id = `2e8948a5-5239-406b-b56b-6c50w3168af3` -* creation_date = 2018-06-25 -* modification_date = 2018-07-24 -* version = 2.0 -* spec_version = 2 - -##### Description -Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. - -##### Narrative -As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ -Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ -Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. - -##### Detections -* Detect New Open S3 buckets -* Detect S3 access from a new IP -* Detect Spike in S3 Bucket deletion - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ - -### Suspicious AWS Traffic -* id = `2e8948a5-5239-406b-b56b-6c50f2168af3` -* creation_date = 2018-05-07 -* modification_date = 2018-05-07 -* version = 1.0 -* spec_version = 2 - -##### Description -Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). - -##### Narrative -A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ -Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ - Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ -The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. - -##### Detections -* Detect Spike in blocked Outbound Traffic from your AWS - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ - -### Unusual AWS EC2 Modifications -* id = `73de57ef-0dfc-411f-b1e7-fa24428aeae0` -* creation_date = 2018-04-09 -* modification_date = 2018-04-09 -* version = 1.0 -* spec_version = 2 - -##### Description -Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. - -##### Narrative -A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ - Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. - -##### Detections -* EC2 Instance Modified With Previously Unseen User - -##### Providing Technologies -* AWS - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - - -## Best Practices - -* [Account Monitoring and Controls](#account-monitoring-and-controls) - -* [Asset Tracking](#asset-tracking) - -* [Monitor Backup Solution](#monitor-backup-solution) - -* [Monitor for Unauthorized Software](#monitor-for-unauthorized-software) - -* [Monitor for Updates](#monitor-for-updates) - -* [Prohibited Traffic Allowed or Protocol Mismatch](#prohibited-traffic-allowed-or-protocol-mismatch) - -* [Router & Infrastructure Security](#router-&-infrastructure-security) - -* [Use of Cleartext Protocols](#use-of-cleartext-protocols) - -### Account Monitoring and Controls -* id = `8892a655-6205-55f7-abba-06460e38c8ae` -* creation_date = 2017-08-05 -* modification_date = 2017-09-06 -* version = 1.0 -* spec_version = 2 - -##### Description -A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. - -##### Narrative -Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. - -##### Detections -* Identify New User Accounts -* Short Lived Windows Accounts -* Detect Excessive Account Lockouts From Endpoint -* Detect Excessive User Account Lockouts - -##### Providing Technologies -* Microsoft Windows -* Active Directory - -##### Data Models -Change -Identity_Management - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf - -### Asset Tracking -* id = `91c676cf-0b23-438d-abee-f6335e1fce77` -* creation_date = 2017-06-01 -* modification_date = 2017-09-13 -* version = 1.0 -* spec_version = 2 - -##### Description -Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. - -##### Narrative -This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. - -##### Detections -* Detect Unauthorized Assets by MAC address - -##### Providing Technologies -* Splunk Stream -* Bro - -##### Data Models -Network_Sessions - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ - -### Monitor Backup Solution -* id = `abe807c7-1eb6-4304-ac32-6e7aacdb891d` -* creation_date = 2017-06-15 -* modification_date = 2017-09-12 -* version = 1.0 -* spec_version = 2 - -##### Description -Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. - -##### Narrative -Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. - -##### Detections -* Unsuccessful Netbackup backups -* Extended Period Without Successful Netbackup Backups - -##### Providing Technologies -* Netbackup - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ - -### Monitor for Unauthorized Software -* id = `8892a655-6205-43f7-abba-06460e38c8ae` -* creation_date = 2017-06-26 -* modification_date = 2017-09-15 -* version = 1.0 -* spec_version = 2 - -##### Description -Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. - -##### Narrative -It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ -It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. - -##### Detections -* Prohibited Software On Endpoint - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ - -### Monitor for Updates -* id = `9ef8d677-7b52-4213-a038-99cfc7acc2d8` -* creation_date = 2017-08-15 -* modification_date = 2017-09-15 -* version = 1.0 -* spec_version = 2 - -##### Description -Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. - -##### Narrative -It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ -Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ -Microsoft releases updates for Windows systems on a monthly cadence. They should be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed. - -##### Detections -* No Windows Updates in a time frame - -##### Providing Technologies -* Microsoft Windows - -##### Data Models -Updates - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://learn.cisecurity.org/20-controls-download - -### Prohibited Traffic Allowed or Protocol Mismatch -* id = `6d13121c-90f3-446d-8ac3-27efbbc65218` -* creation_date = 2017-04-18 -* modification_date = 2017-09-11 -* version = 1.0 -* spec_version = 2 - -##### Description -Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. - -##### Narrative -A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. - -##### Detections -* TOR Traffic -* Prohibited Network Traffic Allowed -* Protocol or Port Mismatch -* Detect hosts connecting to dynamic domain providers - -##### Providing Technologies -* Splunk Stream -* Bro -* Palo Alto Firewall - -##### Data Models -Network_Resolution -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ - -### Router & Infrastructure Security -* id = `91c676cf-0b23-438d-abee-f6335e177e77` -* creation_date = 2017-06-01 -* modification_date = 2017-09-12 -* version = 1.0 -* spec_version = 2 - -##### Description -Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. - -##### Narrative -Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ -This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. - -##### Detections -* Detect New Login Attempts to Routers - -##### Providing Technologies -* Active Directory -* Palo Alto Firewall - -##### Data Models -Authentication - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html -* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html - -### Use of Cleartext Protocols -* id = `826e6431-aeef-41b4-9fc0-6d0985d65a21` -* creation_date = 2016-09-13 -* modification_date = 2017-09-15 -* version = 1.0 -* spec_version = 2 - -##### Description -Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. - -##### Narrative -Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. - -##### Detections -* Protocols passing authentication in cleartext - -##### Providing Technologies -* Splunk Stream -* Bro - -##### Data Models -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.monkey.org/~dugsong/dsniff/ - - -## Vulnerability - -* [Apache Struts Vulnerability](#apache-struts-vulnerability) - -* [JBoss Vulnerability](#jboss-vulnerability) - -* [Spectre And Meltdown Vulnerabilities](#spectre-and-meltdown-vulnerabilities) - -* [Splunk Enterprise Vulnerability](#splunk-enterprise-vulnerability) - -* [Splunk Enterprise Vulnerability CVE-2018-11409](#splunk-enterprise-vulnerability-cve-2018-11409) - -### Apache Struts Vulnerability -* id = `2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e` -* creation_date = 2017-03-14 -* modification_date = 2018-12-06 -* version = 1.0 -* spec_version = 2 - -##### Description -Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. - -##### Narrative -In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ -The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ -This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ -The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ -First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ -hen looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ -Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ -Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ -Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ -hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ -hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ -In the event that a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that have the file open, what processes created and/or modified the file, and the number of systems that may have this file can help to determine if the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes quickly help determine whether it is malicious in nature.\ -Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, there may be activity initiated via a compromised website the user visited.\ -It can also be very helpful to examine various behaviors of the process of interest or the parent of the process that is of interest. For example, if it turns out that the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might also be worth further scrutiny. If a process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. - -##### Detections -* Unusually Long Content-Type Length -* Web Servers Executing Suspicious Processes -* Suspicious Java Classes - -##### Providing Technologies -* Splunk Stream -* Bro -* Bluecoat -* Apache -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Jose Hernandez -* email = jhernandez@splunk.com -* company = Splunk - -##### References -* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf - -### JBoss Vulnerability -* id = `1f5294cb-b85f-4c2d-9c58-ffcf248f52bd` -* creation_date = 2016-10-04 -* modification_date = 2017-09-14 -* version = 1.0 -* spec_version = 2 - -##### Description -In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. - -##### Narrative -This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ -It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ -hen looking at the target of the behavior uncovered by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to identify other recent events involving the target. This can help tie different events together and give further situational awareness regarding the target host.\ -Various types of information for external systems should be reviewed and, potentially, collected if the incident is, indeed, judged to be malicious. This data may be useful for generating your own threat intelligence, so you can create future alerts.\ -The following factors may assist you in determining whether the event is malicious: \ -1. Country of origin\ -1. Responsible party\ -1. Fully qualified domain names associated with the external IP address\ -1. Registration of fully qualified domain names associated with external IP address Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you qualify and understand the event and possible motivation for the attack. In addition, there are various sources that may provide reputation information on the IP address or domain name, which can assist you in determining whether the event is malicious in nature. Finally, determining whether there are other events associated with the IP address may help connect data points or expose other historic events that might be brought back into scope.\ -Gathering various data on the system of interest can sometimes help quickly determine whether something suspicious is happening. Some of these items include determining who else may have logged into the system recently, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and/or whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ -hen a specific service or application is targeted, it is often helpful to know the associated version, to help determine whether it is vulnerable to a specific exploit.\ -If you suspect an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ -If a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that opened the file, the processes that may have created and/or modified the file, and how many other systems potentially have this file can you determine whether the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes help you quickly determine if it is malicious in nature.\ -Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if svchost.exe is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. \ -It can also be helpful to examine various behaviors of and the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see whether the parent process spawned other processes that might also warrant further scrutiny. If a process is suspect, a review of the network connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. - -##### Detections -* Detect malicious requests to exploit JBoss servers -* Detect attackers scanning for vulnerable JBoss servers - -##### Providing Technologies -* Splunk Stream -* Palo Alto Firewall -* Apache -* Bro - -##### Data Models -Web - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html - -### Spectre And Meltdown Vulnerabilities -* id = `6d3306f6-bb2b-4219-8609-8efad64032f2` -* creation_date = 2018-01-08 -* modification_date = 2018-01-08 -* version = 1.0 -* spec_version = 2 - -##### Description -Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. - -##### Narrative -Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. - -##### Detections -* Spectre and Meltdown Vulnerable Systems - -##### Providing Technologies -* Nessus -* Qualys - -##### Data Models -Vulnerabilities - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://meltdownattack.com/ - -### Splunk Enterprise Vulnerability -* id = `4e692b96-de2d-4bd1-9105-37e2368a8db1` -* creation_date = 2016-09-13 -* modification_date = 2017-09-19 -* version = 1.0 -* spec_version = 2 - -##### Description -Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. - -##### Narrative -This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ -\ -1. Splunk Enterprise 6.4.x, prior to 6.4.3\ -1. Splunk Enterprise 6.3.x, prior to 6.3.6\ -1. Splunk Enterprise 6.2.x, prior to 6.2.10\ -1. Splunk Enterprise 6.1.x, prior to 6.1.11\ -1. Splunk Enterprise 6.0.x, prior to 6.0.12\ -1. Splunk Enterprise 5.0.x, prior to 5.0.16\ -1. Splunk Light, prior to 6.4.3CVE-2016-4859 allows attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. (Credit: Noriaki Iwasaki, Cyber Defense Institute, Inc.).\ -It is important to ensure that your Splunk deployment is being kept up to date and is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites. - -##### Detections -* Open Redirect in Splunk Web - -##### Providing Technologies -* Splunk Enterprise - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* http://www.splunk.com/view/SP-CAAAPQ6#announce -* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 - -### Splunk Enterprise Vulnerability CVE-2018-11409 -* id = `1fc34cbc-34e9-43ba-87ab-6811c9e95400` -* creation_date = 2018-06-14 -* modification_date = 2018-06-14 -* version = 1.0 -* spec_version = 2 - -##### Description -Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. - -##### Narrative -Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ -Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ -Read more in Splunk's official response: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings.\ -A detection search within this Analytic Story looks for vulnerabilities described in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip. - -##### Detections -* Splunk Enterprise Information Disclosure - -##### Providing Technologies -* Splunk Enterprise - -##### Data Models - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 -* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings -* https://www.exploit-db.com/exploits/44865/ +* [Vulnerability](#Vulnerability) ## Abuse -* [Brand Monitoring](#brand-monitoring) +* [Brand Monitoring](#Brand-Monitoring) -* [DNS Amplification Attacks](#dns-amplification-attacks) +* [Data Protection](#Data-Protection) -* [Data Protection](#data-protection) +* [DNS Amplification Attacks](#DNS-Amplification-Attacks) -* [Host Redirection](#host-redirection) +* [Host Redirection](#Host-Redirection) -* [Netsh Abuse](#netsh-abuse) +* [Netsh Abuse](#Netsh-Abuse) -* [Web Fraud Detection](#web-fraud-detection) +* [Web Fraud Detection](#Web-Fraud-Detection) ### Brand Monitoring -* id = `91c676cf-0b23-438d-abee-f6335e1fce78` -* creation_date = 2017-06-01 -* modification_date = 2017-12-19 -* version = 1.0 -* spec_version = 2 +* id = 91c676cf-0b23-438d-abee-f6335e1fce78 +* date = 2017-12-19 +* version = 1 -##### Description +#### Description Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. -##### Narrative +#### Narrative While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ Notable events will include IP addresses, URLs, and user data. Drilling down can provide you with even more actionable intelligence, including likely geographic information, contextual searches to help you scope the problem, and investigative searches. -##### Detections +#### Detections * Monitor DNS For Brand Abuse * Monitor Email For Brand Abuse * Monitor Web Traffic For Brand Abuse -##### Providing Technologies -* Splunk Stream -* Bro -* Microsoft Exchange -* Bluecoat -* Palo Alto Firewall +#### Data Models +* Email +* Network_Resolution +* Web -##### Data Models -Network_Resolution -Email -Web +#### Mappings -##### Mappings +##### ATT&CK -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives +* Delivery ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 7 -###### NIST +##### NIST * PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk ##### References * https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ * https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ * https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ -### DNS Amplification Attacks -* id = `e8afd39e-3294-11e6-b39d-a45e60c6700` -* creation_date = 2016-08-24 -* modification_date = 2016-09-13 -* version = 1.0 -* spec_version = 2 - -##### Description -DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. - -##### Narrative -The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ -The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. - -##### Detections -* Large Volume of DNS ANY Queries - -##### Providing Technologies -* Splunk Stream -* Bro - -##### Data Models -Network_Resolution - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.us-cert.gov/ncas/alerts/TA13-088A -* https://www.imperva.com/learn/application-security/dns-amplification/ - ### Data Protection -* id = `91c676cf-0b23-438d-abee-f6335e1fce33` -* creation_date = 2017-06-01 -* modification_date = 2017-09-14 -* version = 1.0 -* spec_version = 2 +* id = 91c676cf-0b23-438d-abee-f6335e1fce33 +* date = 2017-09-14 +* version = 1 -##### Description +#### Description Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. -##### Narrative +#### Narrative Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. -##### Detections -* Detection of DNS Tunnels +#### Detections * Detect USB device insertion * Detect hosts connecting to dynamic domain providers +* Detection of DNS Tunnels -##### Providing Technologies -* Microsoft Windows -* Splunk Stream -* Bro +#### Data Models +* Change_Analysis +* Network_Resolution -##### Data Models -Change_Analysis -Network_Resolution +#### Mappings -##### Mappings +##### ATT&CK +* T1043 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control +* Installation ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 13 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE +##### NIST * DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.DS +* PR.PT ##### References * https://www.cisecurity.org/controls/data-protection/ * https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 * https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ -### Host Redirection -* id = `2e8948a5-5239-406b-b56b-6c50fe268af4` -* creation_date = 2017-06-18 -* modification_date = 2017-09-14 -* version = 1.0 -* spec_version = 2 +### DNS Amplification Attacks +* id = e8afd39e-3294-11e6-b39d-a45e60c6700 +* date = 2016-09-13 +* version = 1 -##### Description +#### Description +DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. + +#### Narrative +The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ +The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. + +#### Detections +* Large Volume of DNS ANY Queries + +#### Data Models +* Network_Resolution + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 12 + +##### NIST +* PR.IP + +##### References +* https://www.us-cert.gov/ncas/alerts/TA13-088A +* https://www.imperva.com/learn/application-security/dns-amplification/ + +### Host Redirection +* id = 2e8948a5-5239-406b-b56b-6c50fe268af4 +* date = 2017-09-14 +* version = 1 + +#### Description Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. -##### Narrative +#### Narrative Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. -##### Detections +#### Detections * Clients Connecting to Multiple DNS Servers * DNS Query Requests Resolved by Unauthorized DNS Servers * Windows hosts file modification -##### Providing Technologies -* Splunk Stream -* Bro -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon +#### Data Models +* Network_Resolution -##### Data Models -Network_Resolution -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1048 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases * Command and Control ###### CIS +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk +* ID.AM +* PR.AC +* PR.DS +* PR.IP +* PR.PT ##### References * https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ ### Netsh Abuse -* id = `2b1800dd-92f9-47ec-a981-fdf1351e5f65` -* creation_date = 2017-01-04 -* modification_date = 2017-01-05 -* version = 1.0 -* spec_version = 2 +* id = 2b1800dd-92f9-47ec-a981-fdf1351e5f65 +* date = 2017-01-05 +* version = 1 -##### Description +#### Description Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. -##### Narrative +#### Narrative It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. -##### Detections +#### Detections * Processes created by netsh * Processes launching netsh -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1059 +* T1089 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS -* CIS 3 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE +##### NIST * DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.PT ##### References * https://technet.microsoft.com/library/bb490939.aspx @@ -1837,16 +236,14 @@ Endpoint * http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html ### Web Fraud Detection -* id = `31337aaa-bc22-4752-b599-ef112dq1dq7a` -* creation_date = 2018-07-12 -* modification_date = 2018-10-08 -* version = 1.0 -* spec_version = 2 +* id = 31337aaa-bc22-4752-b599-ef112dq1dq7a +* date = 2018-10-08 +* version = 1 -##### Description +#### Description Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. -##### Narrative +#### Narrative The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ hen developing a strategy for preventing fraud in your environment, its important to look across all of your web services for evidence that attackers are abusing enterprise resources to enumerate systems, harvest data for secondary fraudulent activity, or abuse terms of service.This Analytic Story looks for evidence of common Internet attack techniques that could be indicative of web fraud in your environmentincluding account harvesting, anomalous user clickspeed, and password sharing across accounts, to name just a few.\ @@ -1854,1133 +251,350 @@ The account-harvesting search focuses on web pages used for user-account registr The anomalous clickspeed search looks for users who are moving through your website at a faster-than-normal speed or with a perfect click cadence (high periodicity or low standard deviation), which could indicate that the user is a script, not an actual human.\ Another search detects incidents wherein a single password is used across multiple accounts, which may indicate that a fraudster has infiltrated your environment and embedded a common password within a script. -##### Detections +#### Detections * Web Fraud - Account Harvesting * Web Fraud - Anomalous User Clickspeed * Web Fraud - Password Sharing Across Accounts -##### Providing Technologies -* Splunk Stream +#### Data Models -##### Data Models +#### Mappings -##### Mappings +##### ATT&CK +* T1078 +* T1136 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 16 +* CIS 6 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Jim Apger -* email = Mayhem@splunk.com -* company = Splunk +* DE.DP ##### References * https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud * https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 -## Malware - -* [ColdRoot MacOS RAT](#coldroot-macos-rat) - -* [DHS Report TA18-074A](#dhs-report-ta18-074a) - -* [Dynamic DNS](#dynamic-dns) - -* [Emotet Malware (DHS Report TA18-201A)](#emotet-malware-(dhs-report-ta18-201a)) - -* [Hidden Cobra Malware](#hidden-cobra-malware) - -* [Orangeworm Attack Group](#orangeworm-attack-group) - -* [Ransomware](#ransomware) - -* [SamSam Ransomware](#samsam-ransomware) - -* [Unusual Processes](#unusual-processes) - -* [Windows File Extension and Association Abuse](#windows-file-extension-and-association-abuse) - -* [Windows Service Abuse](#windows-service-abuse) - -### ColdRoot MacOS RAT -* id = `bd91a2bc-d20b-4f44-a982-1bea98e86390` -* creation_date = 2019-01-29 -* modification_date = 2019-01-09 -* version = 1.0 -* spec_version = 2 - -##### Description -Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. - -##### Narrative -Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ -This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ -Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. - -##### Detections -* Processes Tapping Keyboard Events -* Osquery pack - ColdRoot detection - -##### Providing Technologies -* OSquery - -##### Data Models -Alerts - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Jose Hernandez -* email = jhernandez@splunk.com -* company = Splunk - -##### References -* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ -* https://objective-see.com/blog/blog_0x2A.html -* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ - -### DHS Report TA18-074A -* id = `0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef` -* creation_date = 2018-03-19 -* modification_date = 2020-01-22 -* version = 2.0 -* spec_version = 2 - -##### Description -Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. - -##### Narrative -The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ -There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ -One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ -Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. - -##### Detections -* SMB Traffic Spike -* SMB Traffic Spike - MLTK -* Processes launching netsh -* Suspicious Reg.exe Process -* Sc.exe Manipulating Windows Services -* Registry Keys Used For Persistence -* Create local admin accounts using net.exe -* Single Letter Process On Endpoint -* Scheduled Task Name Used by Dragonfly Threat Actors -* Malicious PowerShell Process - Execution Policy Bypass -* Detect Outbound SMB Traffic -* Detect New Local Admin account -* Detect PsExec With accepteula Flag -* First time seen command line argument - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Windows -* Bro -* Splunk Stream - -##### Data Models -Endpoint -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://www.us-cert.gov/ncas/alerts/TA18-074A - -### Dynamic DNS -* id = `8169f17b-ef68-4b59-aae8-586907301221` -* creation_date = 2017-11-21 -* modification_date = 2018-09-06 -* version = 2.0 -* spec_version = 2 - -##### Description -Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. - -##### Narrative -Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. - -##### Detections -* Detect hosts connecting to dynamic domain providers -* Detect web traffic to dynamic domain providers - -##### Providing Technologies -* Splunk Stream -* Bro -* Bluecoat -* Palo Alto Firewall - -##### Data Models -Network_Resolution -Web - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html -* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ -* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ -* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html - -### Emotet Malware (DHS Report TA18-201A) -* id = `bb9f5ed2-916e-4364-bb6d-91c310efcf52` -* creation_date = 2018-09-11 -* modification_date = 2020-01-27 -* version = 1.0 -* spec_version = 2 - -##### Description -Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. - -##### Narrative -The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ -According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ -The searches in this Analytic Story will help you find executables that are rarely used in your environment, specific registry paths that malware often uses to ensure survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that Emotet or other malware has compromised your environment. - -##### Detections -* Detect Rare Executables -* Registry Keys Used For Persistence -* Detect Use of cmd.exe to Launch Script Interpreters -* Prohibited Software On Endpoint -* SMB Traffic Spike -* SMB Traffic Spike - MLTK -* Suspicious Email Attachment Extensions -* Email Attachments With Lots Of Spaces -* Detection of tools built by NirSoft - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Exchange -* Bro -* Splunk Stream - -##### Data Models -Endpoint -Email -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.us-cert.gov/ncas/alerts/TA18-201A -* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf -* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html - -### Hidden Cobra Malware -* id = `baf7580b-d4b4-4774-8173-7d198e9da335` -* creation_date = 2018-06-14 -* modification_date = 2020-01-22 -* version = 2.0 -* spec_version = 2 - -##### Description -Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. - -##### Narrative -North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ -These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ -In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ -Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. - -##### Detections -* SMB Traffic Spike -* SMB Traffic Spike - MLTK -* First time seen command line argument -* Detect Outbound SMB Traffic -* Remote Desktop Network Traffic -* Remote Desktop Process Running On System -* DNS Query Length With High Standard Deviation -* Create or delete hidden shares using net.exe -* Suspicious File Write -* DNS Query Length Outliers - MLTK - -##### Providing Technologies -* Splunk Stream -* Bro -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Network_Resolution -Network_Traffic -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity -* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf - -### Orangeworm Attack Group -* id = `bb9f5ed2-916e-4364-bb6d-97c370efcf52` -* creation_date = 2018-06-14 -* modification_date = 2020-01-22 -* version = 2.0 -* spec_version = 2 - -##### Description -Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. - -##### Narrative -In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ -Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ -Although the group's motivation is unknown, its goal may be stealing patient information to sell on the black market. Another possible explanation is corporate espionage. \ -Healthcare may be a promising target, because it is notoriously behind in technology, often using older operating systems and neglecting to patch computers. Even so, the group was able to evade detection for a full three years. Sources say that the malware spread quickly within the target networks, infecting computers used to control medical devices, such as MRI and X-ray machines.\ -This Analytic Story is designed to help you detect and investigate suspicious activities that may be indicative of an Orangeworm attack. One detection search looks for command-line arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised. - -##### Detections -* First time seen command line argument -* Sc.exe Manipulating Windows Services -* First Time Seen Running Windows Service - -##### Providing Technologies -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia -* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ - -### Ransomware -* id = `cf309d0d-d4aa-4fbb-963d-1e79febd3756` -* creation_date = 2017-07-24 -* modification_date = 2020-02-04 -* version = 1.1 -* spec_version = 2 - -##### Description -Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. - -##### Narrative -Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. - -##### Detections -* Windows Event Log Cleared -* Suspicious wevtutil Usage -* USN Journal Deletion -* Deleting Shadow Copies -* Spike in File Writes -* Prohibited Network Traffic Allowed -* SMB Traffic Spike -* SMB Traffic Spike - MLTK -* Common Ransomware Extensions -* Common Ransomware Notes -* System Processes Run From Unexpected Locations -* Remote Process Instantiation via WMI -* TOR Traffic -* Registry Keys Used For Persistence -* Unusually Long Command Line -* Unusually Long Command Line - MLTK -* Scheduled tasks used in BadRabbit ransomware -* Schtasks used for forcing a reboot - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Palo Alto Firewall -* Bro -* Splunk Stream -* Microsoft Windows - -##### Data Models -Endpoint -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk - -##### References -* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ -* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html - -### SamSam Ransomware -* id = `c4b89506-fbcf-4cb7-bfd6-527e54789604` -* creation_date = 2018-12-13 -* modification_date = 2018-12-13 -* version = 1.0 -* spec_version = 2 - -##### Description -Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. - -##### Narrative -The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ -Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ -SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ -In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ -According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ -This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. - -##### Detections -* Deleting Shadow Copies -* Spike in File Writes -* Common Ransomware Extensions -* Common Ransomware Notes -* Prohibited Software On Endpoint -* Detect PsExec With accepteula Flag -* Remote Desktop Network Traffic -* Detect attackers scanning for vulnerable JBoss servers -* Detect malicious requests to exploit JBoss servers -* Remote Desktop Network Bruteforce -* File with Samsam Extension -* Samsam Test File Write -* Batch File Write to System32 - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Splunk Stream -* Palo Alto Firewall -* Apache -* Bro - -##### Data Models -Endpoint -Web -Network_Traffic - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ -* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ -* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html - -### Unusual Processes -* id = `f4368e3f-d59f-4192-84f6-748ac5a3ddb6` -* creation_date = 2016-08-09 -* modification_date = 2020-02-04 -* version = 2.1 -* spec_version = 2 - -##### Description -Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. - -##### Narrative -Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ -This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ -In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. - -##### Detections -* Uncommon Processes On Endpoint -* Unusually Long Command Line -* Unusually Long Command Line - MLTK -* Detect Rare Executables -* System Processes Run From Unexpected Locations -* RunDLL Loading DLL By Ordinal -* Detect processes used for System Network Configuration Discovery - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - -##### References -* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html -* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf -* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 - -### Windows File Extension and Association Abuse -* id = `30552a76-ac78-48e4-b3c0-de4e34e9563d` -* creation_date = 2018-01-26 -* modification_date = 2018-01-26 -* version = 1.0 -* spec_version = 2 - -##### Description -Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. - -##### Narrative -Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ - Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ - Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ -Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ -Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. - -##### Detections -* Execution of File with Multiple Extensions -* Execution of File With Spaces Before Extension -* Suspicious Changes to File Associations - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ -* https://attack.mitre.org/wiki/Technique/T1042 - -### Windows Service Abuse -* id = `6dbd810e-f66d-414b-8dfc-e46de55cbfe2` -* creation_date = 2017-11-02 -* modification_date = 2017-11-02 -* version = 3.0 -* spec_version = 2 - -##### Description -Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. - -##### Narrative -The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. - -##### Detections -* Sc.exe Manipulating Windows Services -* Reg.exe Manipulating Windows Services Registry Keys -* First Time Seen Running Windows Service - -##### Providing Technologies -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://attack.mitre.org/wiki/Technique/T1050 -* https://attack.mitre.org/wiki/Technique/T1031 - - ## Adversary Tactics -* [Collection and Staging](#collection-and-staging) +* [Collection and Staging](#Collection-and-Staging) -* [Command and Control](#command-and-control) +* [Command and Control](#Command-and-Control) -* [Common Phishing Frameworks](#common-phishing-frameworks) +* [Common Phishing Frameworks](#Common-Phishing-Frameworks) -* [Credential Dumping](#credential-dumping) +* [Credential Dumping](#Credential-Dumping) -* [DNS Hijacking](#dns-hijacking) +* [Disabling Security Tools](#Disabling-Security-Tools) -* [Disabling Security Tools](#disabling-security-tools) +* [DNS Hijacking](#DNS-Hijacking) -* [Lateral Movement](#lateral-movement) +* [Lateral Movement](#Lateral-Movement) -* [Malicious PowerShell](#malicious-powershell) +* [Malicious PowerShell](#Malicious-PowerShell) -* [Phishing Payloads](#phishing-payloads) +* [Phishing Payloads](#Phishing-Payloads) -* [Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns](#possible-backdoor-activity-associated-with-mudcarp-espionage-campaigns) +* [Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns](#Possible-Backdoor-Activity-Associated-With-MUDCARP-Espionage-Campaigns) -* [SQL Injection](#sql-injection) +* [SQL Injection](#SQL-Injection) -* [Suspicious Command-Line Executions](#suspicious-command-line-executions) +* [Suspicious Command-Line Executions](#Suspicious-Command-Line-Executions) -* [Suspicious DNS Traffic](#suspicious-dns-traffic) +* [Suspicious DNS Traffic](#Suspicious-DNS-Traffic) -* [Suspicious Emails](#suspicious-emails) +* [Suspicious Emails](#Suspicious-Emails) -* [Suspicious MSHTA Activity](#suspicious-mshta-activity) +* [Suspicious MSHTA Activity](#Suspicious-MSHTA-Activity) -* [Suspicious Okta Activity](#suspicious-okta-activity) +* [Suspicious Okta Activity](#Suspicious-Okta-Activity) -* [Suspicious WMI Use](#suspicious-wmi-use) +* [Suspicious Windows Registry Activities](#Suspicious-Windows-Registry-Activities) -* [Suspicious Windows Registry Activities](#suspicious-windows-registry-activities) +* [Suspicious WMI Use](#Suspicious-WMI-Use) -* [Windows Defense Evasion Tactics](#windows-defense-evasion-tactics) +* [Windows Defense Evasion Tactics](#Windows-Defense-Evasion-Tactics) -* [Windows Log Manipulation](#windows-log-manipulation) +* [Windows Log Manipulation](#Windows-Log-Manipulation) -* [Windows Persistence Techniques](#windows-persistence-techniques) +* [Windows Persistence Techniques](#Windows-Persistence-Techniques) -* [Windows Privilege Escalation](#windows-privilege-escalation) +* [Windows Privilege Escalation](#Windows-Privilege-Escalation) ### Collection and Staging -* id = `8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a` -* creation_date = 2018-01-08 -* modification_date = 2020-02-03 -* version = 1.1 -* spec_version = 2 +* id = 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a +* date = 2020-02-03 +* version = 1 -##### Description +#### Description Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. -##### Narrative +#### Narrative A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ Use the searches to detect and monitor suspicious behavior related to these activities. -##### Detections -* Suspicious writes to windows Recycle Bin -* Suspicious writes to System Volume Information +#### Detections * Email files written outside of the Outlook directory -* Hosts receiving high volume of network traffic from email server * Email servers sending high volume traffic to hosts +* Hosts receiving high volume of network traffic from email server +* Suspicious writes to System Volume Information +* Suspicious writes to windows Recycle Bin -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Bro -* Splunk Stream +#### Data Models +* Endpoint +* Network_Traffic -##### Data Models -Endpoint -Network_Traffic +#### Mappings -##### Mappings +##### ATT&CK +* T1043 +* T1074 +* T1114 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS -* CIS 3 +* CIS 7 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk +* PR.PT ##### References * https://attack.mitre.org/wiki/Collection * https://attack.mitre.org/wiki/Technique/T1074 ### Command and Control -* id = `943773c6-c4de-4f38-89a8-0b92f98804d8` -* creation_date = 2018-06-01 -* modification_date = 2018-06-01 -* version = 1.0 -* spec_version = 2 +* id = 943773c6-c4de-4f38-89a8-0b92f98804d8 +* date = 2018-06-01 +* version = 1 -##### Description +#### Description Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. -##### Narrative +#### Narrative Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. -##### Detections -* Detect Large Outbound ICMP Packets -* Protocol or Port Mismatch -* Detection of DNS Tunnels -* TOR Traffic -* Prohibited Network Traffic Allowed +#### Detections * Clients Connecting to Multiple DNS Servers -* DNS Query Length With High Standard Deviation -* Detect hosts connecting to dynamic domain providers -* Excessive DNS Failures -* Detect Long DNS TXT Record Response -* DNS Query Requests Resolved by Unauthorized DNS Servers -* Detect Spike in blocked Outbound Traffic from your AWS * DNS Query Length Outliers - MLTK +* DNS Query Length With High Standard Deviation +* DNS Query Requests Resolved by Unauthorized DNS Servers +* Detect Large Outbound ICMP Packets +* Detect Long DNS TXT Record Response +* Detect Spike in blocked Outbound Traffic from your AWS +* Detect hosts connecting to dynamic domain providers +* Detection of DNS Tunnels +* Excessive DNS Failures +* Prohibited Network Traffic Allowed +* Protocol or Port Mismatch +* TOR Traffic -##### Providing Technologies -* Splunk Stream -* Bro -* Palo Alto Firewall -* AWS +#### Data Models +* Network_Resolution +* Network_Traffic -##### Data Models -Network_Resolution -Network_Traffic +#### Mappings -##### Mappings +##### ATT&CK +* T1043 +* T1048 +* T1095 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control +* Delivery ###### CIS +* CIS 1 +* CIS 11 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 +* CIS 9 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk +* ID.AM +* PR.AC +* PR.DS +* PR.IP +* PR.PT ##### References * https://attack.mitre.org/wiki/Command_and_Control * https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware ### Common Phishing Frameworks -* id = `9a64ab44-9214-4639-8163-7eaa2621bd61` -* creation_date = 2019-04-29 -* modification_date = 2019-04-29 -* version = 1.0 -* spec_version = 2 +* id = 9a64ab44-9214-4639-8163-7eaa2621bd61 +* date = 2019-04-29 +* version = 1 -##### Description +#### Description Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. -##### Narrative +#### Narrative As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2. -##### Detections +#### Detections * Detect DNS requests to Phishing Sites leveraging EvilGinx2 -##### Providing Technologies -* Splunk Stream -* Bro +#### Data Models +* Network_Resolution -##### Data Models -Network_Resolution -Web +#### Mappings -##### Mappings +##### ATT&CK +* T1192 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases * Command and Control ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 7 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE +##### NIST * DE.CM -##### Maintainers -* name = Splunk Research Team -* email = research@splunk.com -* company = Splunk - ##### References * https://github.com/kgretzky/evilginx2 * https://attack.mitre.org/techniques/T1192/ * https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ ### Credential Dumping -* id = `854d78bf-d0e2-4f4e-b05c-640905f86d7a` -* creation_date = 2018-08-08 -* modification_date = 2020-02-04 -* version = 3.0 -* spec_version = 2 +* id = 854d78bf-d0e2-4f4e-b05c-640905f86d7a +* date = 2020-02-04 +* version = 3 -##### Description +#### Description Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. -##### Narrative +#### Narrative Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ The detection searches in this Analytic Story monitor access to the Local Security Authority Subsystem Service (LSASS) process, the usage of shadowcopies for credential dumping and some other techniques for credential dumping. -##### Detections +#### Detections * Access LSASS Memory for Dump Creation -* Create Remote Thread into LSASS -* Detect Credential Dumping through LSASS access -* Unsigned Image Loaded by LSASS -* Attempted Credential Dump From Registry via Reg.exe -* Detect Mimikatz Using Loaded Images * Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +* Attempted Credential Dump From Registry via Reg exe +* Create Remote Thread into LSASS * Creation of Shadow Copy * Creation of Shadow Copy with wmic and powershell -* Credential Dumping via Copy Command from Shadowcopy -* Credential Dumping via Symlink to Shadowcopy +* Credential Dumping via Copy Command from Shadow Copy +* Credential Dumping via Symlink to Shadow Copy +* Detect Credential Dumping through LSASS access +* Detect Mimikatz Using Loaded Images * Dump LSASS via comsvcs DLL +* Unsigned Image Loaded by LSASS -##### Providing Technologies -* Microsoft Windows -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1003 +* T1064 +* T1086 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives +* Installation ###### CIS +* CIS 16 * CIS 3 +* CIS 5 +* CIS 6 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk -* name = Patrick Bareiss -* email = pbareiss@splunk.com -* company = Splunk +* PR.AC +* PR.IP ##### References * https://attack.mitre.org/wiki/Technique/T1003 * https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html -### DNS Hijacking -* id = `8169f17b-ef68-4b59-aa28-586907301221` -* creation_date = 2017-11-21 -* modification_date = 2020-02-04 -* version = 1.0 -* spec_version = 2 +### Disabling Security Tools +* id = fcc27099-46a0-46b0-a271-5c7dab56b6f1 +* date = 2020-02-04 +* version = 2 -##### Description +#### Description +Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. + +#### Narrative +Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). + +#### Detections +* Attempt To Add Certificate To Untrusted Store +* Attempt To Stop Security Service +* Processes launching netsh +* Sc exe Manipulating Windows Services +* Suspicious Reg exe Process +* Unload Sysmon Filter Driver + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1031 +* T1050 +* T1059 +* T1089 +* T1112 + +##### Kill Chain Phases +* Actions on Objectives +* Installation + +###### CIS +* CIS 3 +* CIS 5 +* CIS 8 + +##### NIST +* DE.CM +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +##### References +* https://attack.mitre.org/wiki/Technique/T1089 +* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf + +### DNS Hijacking +* id = 8169f17b-ef68-4b59-aa28-586907301221 +* date = 2020-02-04 +* version = 1 + +#### Description Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. -##### Narrative +#### Narrative Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days: \ @@ -2991,44 +605,38 @@ On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.\ The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment. -##### Detections -* DNS record changed +#### Detections * Clients Connecting to Multiple DNS Servers -* Detect hosts connecting to dynamic domain providers * DNS Query Requests Resolved by Unauthorized DNS Servers +* DNS record changed +* Detect hosts connecting to dynamic domain providers -##### Providing Technologies -* Splunk Stream -* Bro +#### Data Models +* Network_Resolution -##### Data Models -Network_Resolution +#### Mappings -##### Mappings +##### ATT&CK +* T1048 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control ###### CIS +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* ID.AM +* PR.DS +* PR.IP +* PR.PT ##### References * https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html @@ -3036,146 +644,63 @@ Network_Resolution * http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ * https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html -### Disabling Security Tools -* id = `fcc27099-46a0-46b0-a271-5c7dab56b6f1` -* creation_date = 2018-04-09 -* modification_date = 2020-02-04 -* version = 2.0 -* spec_version = 2 - -##### Description -Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. - -##### Narrative -Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). - -##### Detections -* Sc.exe Manipulating Windows Services -* Suspicious Reg.exe Process -* Processes launching netsh -* Attempt To Stop Security Service -* Attempt To Add Certificate To Untrusted Store -* Unload Sysmon Filter Driver - -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://attack.mitre.org/wiki/Technique/T1089 -* https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ -* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf - ### Lateral Movement -* id = `399d65dc-1f08-499b-a259-aad9051f38ad` -* creation_date = 2016-09-13 -* modification_date = 2020-02-04 -* version = 2.0 -* spec_version = 2 +* id = 399d65dc-1f08-499b-a259-aad9051f38ad +* date = 2020-02-04 +* version = 2 -##### Description +#### Description Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. -##### Narrative +#### Narrative Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ An adversary can use lateral movement for multiple purposes, including remote execution of tools, pivoting to additional systems, obtaining access to specific information or files, access to additional credentials, exfiltrating data, or delivering a secondary effect. Adversaries may use legitimate credentials alongside inherent network and operating-system functionality to remotely connect to other systems and remain under the radar of network defenders.\ If there is evidence of lateral movement, it is imperative for analysts to collect evidence of the associated offending hosts. For example, an attacker might leverage host A to gain access to host B. From there, the attacker may try to move laterally to host C. In this example, the analyst should gather as much information as possible from all three hosts. \ It is also important to collect authentication logs for each host, to ensure that the offending accounts are well-documented. Analysts should account for all processes to ensure that the attackers did not install unauthorized software. -##### Detections -* Schtasks scheduling job on remote system -* Remote Desktop Process Running On System -* Remote Desktop Network Traffic +#### Detections * Detect Activity Related to Pass the Hash Attacks +* Remote Desktop Network Traffic +* Remote Desktop Process Running On System +* Schtasks scheduling job on remote system -##### Providing Technologies -* Microsoft Windows -* Bro -* Splunk Stream -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint +* Network_Traffic -##### Data Models -Network_Traffic -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1053 +* T1075 +* T1076 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS +* CIS 16 * CIS 3 -* CIS 8 -* CIS 12 +* CIS 9 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk +* PR.AC +* PR.IP ##### References -* https://www.binarydefense.com/reliably-detecting-pass-the-hash/ * https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html ### Malicious PowerShell -* id = `2c8ff66e-0b57-42af-8ad7-912438a403fc` -* creation_date = 2016-09-18 -* modification_date = 2017-08-23 -* version = 4.0 -* spec_version = 2 +* id = 2c8ff66e-0b57-42af-8ad7-912438a403fc +* date = 2017-08-23 +* version = 4 -##### Description +#### Description Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. -##### Narrative +#### Narrative The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ 1. Country of origin\ @@ -3187,64 +712,49 @@ Often, a simple inspection of the process name and path can tell you if the syst It can also be very helpful to examine various behaviors of the process of interest or the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might be worth further scrutiny. If a process is suspect, a review of the network connections made in and around the time of the event and/or whether the process spawned any child processes could be helpful, as well.\ In the event a system is suspected of having been compromised via a malicious website, we suggest reviewing the browsing activity from that system around the time of the event. If categories are given for the URLs visited, that can help you zero in on possible malicious sites. -##### Detections +#### Detections +* Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass * Malicious PowerShell Process - Connect To Internet With Hidden Window * Malicious PowerShell Process - Encoded Command * Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments * Malicious PowerShell Process With Obfuscation Techniques -* Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1064 +* T1086 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control ###### CIS * CIS 3 +* CIS 7 * CIS 8 -* CIS 12 -###### NIST +##### NIST +* DE.CM * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk ##### References * https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ * https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ ### Phishing Payloads -* id = `57226b40-94f3-4ce5-b101-a75f67759c27` -* creation_date = 2019-04-29 -* modification_date = 2019-04-29 -* version = 1.0 -* spec_version = 2 +* id = 57226b40-94f3-4ce5-b101-a75f67759c27 +* date = 2019-04-29 +* version = 1 -##### Description +#### Description Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. -##### Narrative +#### Narrative Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ hile any kind of file may contain a malicious payload, some are more likely to be perceived as benign (and thus more often escape notice) by the average victim—especially when the attacker sends an email that seems to be from one of their contacts. An example is Microsoft Office files. Most corporate users are familiar with documents with the following suffixes: .doc/.docx (MS Word), .xls/.xlsx (MS Excel), and .ppt/.pptx (MS PowerPoint), so they may click without a second thought, slashing a hole in their organizations' security. \ @@ -3254,60 +764,41 @@ Following is a typical series of events, according to an [article by Trend Micro 1. Powershell executes a reverse shell, rendering the exploit successful As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ This Analytic Story focuses on detecting signs that a malicious payload has been injected into your environment. For example, one search detects outlook.exe writing a .zip file. Another looks for suspicious .lnk files launching processes. -##### Detections -* Detect Oulook.exe writing a .zip file +#### Detections +* Detect Oulook exe writing a zip file * Suspicious LNK file launching a process -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1193 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives +* Installation ###### CIS -* CIS 3 +* CIS 7 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Splunk Research Team -* email = research@splunk.com -* company = Splunk +##### NIST +* ID.AM +* PR.DS ##### References * https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html ### Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns -* id = `988C59C5-0A1C-45B6-A555-0C62276E327E` -* creation_date = 2018-07-24 -* modification_date = 2020-01-22 -* version = 1.0 -* spec_version = 2 +* id = 988C59C5-0A1C-45B6-A555-0C62276E327E +* date = 2020-01-22 +* version = 1 -##### Description +#### Description Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. -##### Narrative +#### Narrative This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ The MUDCARP techniques include the use of the compressed-folders module from Microsoft, zipfldr.dll, with RouteTheCall export to run the malicious process or command. After a successful reboot, the malware is made persistent by a manipulating `[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run]'help'='c:\\windows\\system32\\rundll32.exe c:\\windows\\system32\\zipfldr.dll,RouteTheCall c:\\programdata\\winapp.exe'`. Though this technique is not exclusive to MUDCARP, it has been spotted in the group's arsenal of advanced techniques seen in the wild.\ @@ -3337,163 +828,122 @@ If behavioral searches included in this story yield positive hits, iDefense reco 1. b8ec65dab97cdef3cd256cc4753f0c54\ 1. 04d83cd3813698de28cfbba326d7647c -##### Detections +#### Detections * First time seen command line argument -* Registry Keys Used For Persistence * Malicious PowerShell Process - Connect To Internet With Hidden Window +* Registry Keys Used For Persistence * Unusually Long Command Line * Unusually Long Command Line - MLTK -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1059 +* T1064 +* T1086 +* T1103 +* T1131 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control ###### CIS * CIS 3 +* CIS 7 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = iDefense Cyber Espionage Team -* email = iDefense.IntelOps@accenture.com -* company = iDefense +* PR.IP +* PR.PT ##### References * https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ * http://blog.amossys.fr/badflick-is-not-so-bad.html ### SQL Injection -* id = `4f6632f5-449c-4686-80df-57625f59bab3` -* creation_date = 2016-09-13 -* modification_date = 2017-09-19 -* version = 1.0 -* spec_version = 2 +* id = 4f6632f5-449c-4686-80df-57625f59bab3 +* date = 2017-09-19 +* version = 1 -##### Description +#### Description Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. -##### Narrative +#### Narrative It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. -##### Detections +#### Detections * SQL Injection with Long URLs -##### Providing Technologies -* Splunk Stream -* Bro +#### Data Models +* Web -##### Data Models -Web +#### Mappings -##### Mappings +##### ATT&CK +* T1043 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Delivery ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 18 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE +##### NIST * DE.CM -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk - ##### References * https://capec.mitre.org/data/definitions/66.html * https://www.incapsula.com/web-application-security/sql-injection.html ### Suspicious Command-Line Executions -* id = `f4368ddf-d59f-4192-84f6-778ac5a3ffc7` -* creation_date = 2017-10-09 -* modification_date = 2020-02-03 -* version = 2.1 -* spec_version = 2 +* id = f4368ddf-d59f-4192-84f6-778ac5a3ffc7 +* date = 2020-02-03 +* version = 2 -##### Description +#### Description Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. -##### Narrative +#### Narrative The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. -##### Detections +#### Detections +* Detect Prohibited Applications Spawning cmd exe +* Detect Use of cmd exe to Launch Script Interpreters * First time seen command line argument +* System Processes Run From Unexpected Locations * Unusually Long Command Line * Unusually Long Command Line - MLTK -* Detect Prohibited Applications Spawning cmd.exe -* Detect Use of cmd.exe to Launch Script Interpreters -* System Processes Run From Unexpected Locations -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1036 +* T1059 +* T1064 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control +* Exploitation ###### CIS * CIS 3 * CIS 8 -* CIS 12 -###### NIST +##### NIST +* DE.CM * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk ##### References * https://attack.mitre.org/wiki/Technique/T1059 @@ -3501,60 +951,54 @@ Endpoint * https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf ### Suspicious DNS Traffic -* id = `3c3835c0-255d-4f9e-ab84-e29ec9ec9b56` -* creation_date = 2016-09-13 -* modification_date = 2017-09-18 -* version = 1.0 -* spec_version = 2 +* id = 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56 +* date = 2017-09-18 +* version = 1 -##### Description +#### Description Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. -##### Narrative +#### Narrative Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. -##### Detections -* Excessive DNS Failures +#### Detections * Clients Connecting to Multiple DNS Servers +* DNS Query Length Outliers - MLTK * DNS Query Length With High Standard Deviation * DNS Query Requests Resolved by Unauthorized DNS Servers * Detect Long DNS TXT Record Response -* Detection of DNS Tunnels * Detect hosts connecting to dynamic domain providers -* DNS Query Length Outliers - MLTK +* Detection of DNS Tunnels +* Excessive DNS Failures -##### Providing Technologies -* Splunk Stream -* Bro +#### Data Models +* Network_Resolution -##### Data Models -Network_Resolution +#### Mappings -##### Mappings +##### ATT&CK +* T1043 +* T1048 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases +##### Kill Chain Phases +* Actions on Objectives * Command and Control ###### CIS +* CIS 1 +* CIS 12 +* CIS 13 * CIS 3 * CIS 8 -* CIS 12 +* CIS 9 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk +* ID.AM +* PR.DS +* PR.IP +* PR.PT ##### References * http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ @@ -3562,121 +1006,88 @@ Network_Resolution * https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 ### Suspicious Emails -* id = `2b1800dd-92f9-47ec-a981-fdf1351e5d55` -* creation_date = 2017-03-24 -* modification_date = 2020-01-27 -* version = 1.0 -* spec_version = 2 +* id = 2b1800dd-92f9-47ec-a981-fdf1351e5d55 +* date = 2020-01-27 +* version = 1 -##### Description +#### Description Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. -##### Narrative +#### Narrative It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ 1. Which users have received this or a similar message in the past?\ 1. When did the targeted campaign begin?\ 1. Have any users interacted with the content of the messages (by downloading an attachment or clicking on a malicious URL)?This Analytic Story provides detection searches to identify suspicious emails, as well as contextual and investigative searches to help answer some of these questions. -##### Detections -* Monitor Email For Brand Abuse -* Suspicious Email Attachment Extensions +#### Detections * Email Attachments With Lots Of Spaces +* Monitor Email For Brand Abuse * Suspicious Email - UBA Anomaly +* Suspicious Email Attachment Extensions -##### Providing Technologies -* Microsoft Exchange -* Bro -* Splunk Stream +#### Data Models +* Email +* UEBA -##### Data Models -Email -UEBA +#### Mappings -##### Mappings +##### ATT&CK -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Delivery ###### CIS -* CIS 3 -* CIS 8 * CIS 12 +* CIS 3 +* CIS 7 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE -* DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.IP ##### References * https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ ### Suspicious MSHTA Activity -* id = `2b1800dd-92f9-47dd-a981-fdf13w1q5d55` -* creation_date = 2018-08-07 -* modification_date = 2020-02-03 -* version = 1.1 -* spec_version = 2 +* id = 2b1800dd-92f9-47dd-a981-fdf13w1q5d55 +* date = 2020-02-03 +* version = 1 -##### Description +#### Description Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. -##### Narrative +#### Narrative One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ The searches in this story help you detect and investigate suspicious activity that may indicate that an attacker is leveraging mshta.exe to execute malicious code. -##### Detections -* Detect mshta.exe running scripts in command-line arguments +#### Detections +* Detect Prohibited Applications Spawning cmd exe +* Detect mshta exe running scripts in command-line arguments * Registry Keys Used For Persistence -* Detect Prohibited Applications Spawning cmd.exe -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1059 +* T1103 +* T1131 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives +* Exploitation ###### CIS -* CIS 3 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.PT ##### References * https://redcanary.com/blog/windows-registry-attacks-threat-detection/ @@ -3684,308 +1095,231 @@ Endpoint * https://attack.mitre.org/wiki/Technique/T1170 ### Suspicious Okta Activity -* id = `9cbd34af-8f39-4476-a423-bacd126c750b` -* creation_date = 2020-04-02 -* modification_date = 2020-04-02 -* version = 1.0 -* spec_version = 2 +* id = 9cbd34af-8f39-4476-a423-bacd126c750b +* date = 2020-04-02 +* version = 1 -##### Description +#### Description Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. -##### Narrative +#### Narrative Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ With people moving quickly to adopt web-based applications and ways to manage them, many are still struggling to understand how best to monitor these environments. This analytic story provides searches to help monitor this environment, and identify events and activity that warrant further investigation such as credential stuffing or password spraying attacks, and users logging in from multiple locations when travel is disallowed. -##### Detections -* Okta Failed SSO Attempts +#### Detections * Multiple Okta Users With Invalid Credentails From The Same IP * Okta Account Lockout Events +* Okta Failed SSO Attempts * Okta User Logins From Multiple Cities -##### Providing Technologies -* Okta +#### Data Models -##### Data Models +#### Mappings -##### Mappings +##### ATT&CK +* T1078 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases ###### CIS -* CIS 3 -* CIS 8 -* CIS 12 +* CIS 16 -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE +##### NIST * DE.CM -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - ##### References * https://attack.mitre.org/wiki/Technique/T1078 * https://owasp.org/www-community/attacks/Credential_stuffing * https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work -### Suspicious WMI Use -* id = `c8ddc5be-69bc-4202-b3ab-4010b27d7ad5` -* creation_date = 2017-01-13 -* modification_date = 2018-10-23 -* version = 2.0 -* spec_version = 2 - -##### Description -Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. - -##### Narrative -WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ -The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ -In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end. - -##### Detections -* Remote WMI Command Attempt -* Remote Process Instantiation via WMI -* WMI Permanent Event Subscription -* WMI Permanent Event Subscription - Sysmon -* WMI Temporary Event Subscription -* Process Execution via WMI -* Script Execution via WMI - -##### Providing Technologies -* Carbon Black Response -* Sysmon -* Tanium -* Ziften -* CrowdStrike Falcon -* Microsoft Windows - -##### Data Models -Endpoint - -##### Mappings - -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control - -###### CIS -* CIS 3 -* CIS 8 -* CIS 12 - -###### NIST -* PR.IP -* PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk - -##### References -* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf -* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html - ### Suspicious Windows Registry Activities -* id = `2b1800dd-92f9-47dd-a981-fdf1351e5d55` -* creation_date = 2018-05-31 -* modification_date = 2018-05-31 -* version = 1.0 -* spec_version = 2 +* id = 2b1800dd-92f9-47dd-a981-fdf1351e5d55 +* date = 2018-05-31 +* version = 1 -##### Description +#### Description Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. -##### Narrative +#### Narrative Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. -##### Detections -* Remote Registry Key modifications -* Suspicious Changes to File Associations +#### Detections * Disabling Remote User Account Control -* Registry Keys for Creating SHIM Databases * Monitor Registry Keys for Print Monitors -* Reg.exe used to hide files/directories via registry keys +* Reg exe used to hide files directories via registry keys * Registry Keys Used For Persistence * Registry Keys Used For Privilege Escalation +* Registry Keys for Creating SHIM Databases +* Remote Registry Key modifications +* Suspicious Changes to File Associations -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint -Change_Analysis +#### Mappings -##### Mappings +##### ATT&CK +* T1015 +* T1042 +* T1103 +* T1112 +* T1131 +* T1138 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS * CIS 3 +* CIS 5 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.AC +* PR.IP +* PR.PT ##### References * https://redcanary.com/blog/windows-registry-attacks-threat-detection/ * https://attack.mitre.org/wiki/Technique/T1112 -### Windows Defense Evasion Tactics -* id = `56e24a28-5003-4047-b2db-e8f3c4618064` -* creation_date = 2017-10-11 -* modification_date = 2018-05-31 -* version = 1.0 -* spec_version = 2 +### Suspicious WMI Use +* id = c8ddc5be-69bc-4202-b3ab-4010b27d7ad5 +* date = 2018-10-23 +* version = 2 -##### Description -Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others +#### Description +Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. -##### Narrative -Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. +#### Narrative +WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ +The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ +In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end. -##### Detections -* Suspicious Reg.exe Process -* Disabling Remote User Account Control -* Hiding Files And Directories With Attrib.exe -* Reg.exe used to hide files/directories via registry keys -* Remote Registry Key modifications +#### Detections +* Process Execution via WMI +* Remote Process Instantiation via WMI +* Remote WMI Command Attempt +* Script Execution via WMI +* WMI Permanent Event Subscription +* WMI Permanent Event Subscription - Sysmon +* WMI Temporary Event Subscription -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1047 +* T1084 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS * CIS 3 -* CIS 8 -* CIS 12 +* CIS 5 -###### NIST +##### NIST +* PR.AC +* PR.AT * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk +##### References +* https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf +* https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html + +### Windows Defense Evasion Tactics +* id = 56e24a28-5003-4047-b2db-e8f3c4618064 +* date = 2018-05-31 +* version = 1 + +#### Description +Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others + +#### Narrative +Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. + +#### Detections +* Disabling Remote User Account Control +* Hiding Files And Directories With Attrib exe +* Reg exe used to hide files directories via registry keys +* Remote Registry Key modifications +* Suspicious Reg exe Process + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1089 +* T1112 + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 8 + +##### NIST +* DE.CM +* PR.PT ##### References * https://attack.mitre.org/wiki/Defense_Evasion ### Windows Log Manipulation -* id = `b6db2c60-a281-48b4-95f1-2cd99ed56835` -* creation_date = 2017-02-17 -* modification_date = 2017-09-12 -* version = 2.0 -* spec_version = 2 +* id = b6db2c60-a281-48b4-95f1-2cd99ed56835 +* date = 2017-09-12 +* version = 2 -##### Description +#### Description Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. -##### Narrative +#### Narrative Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). -##### Detections +#### Detections * Deleting Shadow Copies -* Windows Event Log Cleared * Suspicious wevtutil Usage * USN Journal Deletion +* Windows Event Log Cleared -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften -* Microsoft Windows +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1070 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives ###### CIS +* CIS 10 * CIS 3 +* CIS 5 +* CIS 6 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = Rico Valdez -* email = rvaldez@splunk.com -* company = Splunk +* DE.DP +* PR.AC +* PR.AT +* PR.IP +* PR.PT ##### References * https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ @@ -3993,71 +1327,60 @@ Endpoint * http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html ### Windows Persistence Techniques -* id = `30874d4f-20a1-488f-85ec-5d52ef74e3f9` -* creation_date = 2017-04-19 -* modification_date = 2018-05-31 -* version = 2.0 -* spec_version = 2 +* id = 30874d4f-20a1-488f-85ec-5d52ef74e3f9 +* date = 2018-05-31 +* version = 2 -##### Description +#### Description Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. -##### Narrative +#### Narrative Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. -##### Detections -* Registry Keys for Creating SHIM Databases -* Shim Database Installation With Suspicious Parameters -* Shim Database File Creation -* Registry Keys Used For Persistence -* Schtasks used for forcing a reboot -* Sc.exe Manipulating Windows Services -* Reg.exe Manipulating Windows Services Registry Keys -* Hiding Files And Directories With Attrib.exe -* Reg.exe used to hide files/directories via registry keys -* Detect Path Interception By Creation Of program.exe +#### Detections +* Detect Path Interception By Creation Of program exe +* Hiding Files And Directories With Attrib exe * Monitor Registry Keys for Print Monitors +* Reg exe Manipulating Windows Services Registry Keys +* Reg exe used to hide files directories via registry keys +* Registry Keys Used For Persistence +* Registry Keys for Creating SHIM Databases * Remote Registry Key modifications +* Sc exe Manipulating Windows Services +* Schtasks used for forcing a reboot +* Shim Database File Creation +* Shim Database Installation With Suspicious Parameters -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Tanium -* Ziften -* Sysmon +#### Data Models +* Endpoint -##### Data Models -Endpoint -Change_Analysis +#### Mappings -##### Mappings +##### ATT&CK +* T1031 +* T1050 +* T1053 +* T1089 +* T1103 +* T1131 +* T1138 -###### ATT&CK -* Command and Control -* Exfiltration - -###### Kill Chain Phases -* Command and Control +##### Kill Chain Phases +* Actions on Objectives +* Installation ###### CIS * CIS 3 +* CIS 5 * CIS 8 -* CIS 12 -###### NIST -* PR.IP -* PR.PT -* PR.AC +##### NIST * DE.AE * DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk -* name = Bhavin Patel -* email = bpatel@splunk.com -* company = Splunk +* PR.AC +* PR.AT +* PR.IP +* PR.PT ##### References * http://www.fuzzysecurity.com/tutorials/19.html @@ -4067,59 +1390,1761 @@ Change_Analysis * https://www.youtube.com/watch?v=dq2Hv7J9fvk ### Windows Privilege Escalation -* id = `644e22d3-598a-429c-a007-16fdb802cae5` -* creation_date = 2017-12-07 -* modification_date = 2020-02-04 -* version = 2.0 -* spec_version = 2 +* id = 644e22d3-598a-429c-a007-16fdb802cae5 +* date = 2020-02-04 +* version = 2 -##### Description +#### Description Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. -##### Narrative +#### Narrative Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. -##### Detections +#### Detections +* Child Processes of Spoolsv exe * Overwriting Accessibility Binaries * Registry Keys Used For Privilege Escalation * Uncommon Processes On Endpoint -* Child Processes of Spoolsv.exe -##### Providing Technologies -* Carbon Black Response -* CrowdStrike Falcon -* Sysmon -* Tanium -* Ziften +#### Data Models +* Endpoint -##### Data Models -Endpoint +#### Mappings -##### Mappings +##### ATT&CK +* T1015 +* T1068 -###### ATT&CK +##### Kill Chain Phases +* Actions on Objectives +* Exploitation + +###### CIS +* CIS 2 +* CIS 8 + +##### NIST +* DE.CM +* ID.AM +* PR.DS +* PR.PT + +##### References +* https://attack.mitre.org/tactics/TA0004/ + + +## Best Practices + +* [Account Monitoring and Controls](#Account-Monitoring-and-Controls) + +* [Asset Tracking](#Asset-Tracking) + +* [Monitor Backup Solution](#Monitor-Backup-Solution) + +* [Monitor for Unauthorized Software](#Monitor-for-Unauthorized-Software) + +* [Monitor for Updates](#Monitor-for-Updates) + +* [Prohibited Traffic Allowed or Protocol Mismatch](#Prohibited-Traffic-Allowed-or-Protocol-Mismatch) + +* [Router and Infrastructure Security](#Router-and-Infrastructure-Security) + +* [Use of Cleartext Protocols](#Use-of-Cleartext-Protocols) + +### Account Monitoring and Controls +* id = 8892a655-6205-55f7-abba-06460e38c8ae +* date = 2017-09-06 +* version = 1 + +#### Description +A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. + +#### Narrative +Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. + +#### Detections +* Detect Excessive Account Lockouts From Endpoint +* Detect Excessive User Account Lockouts +* Identify New User Accounts +* Short Lived Windows Accounts + +#### Data Models +* Change + +#### Mappings + +##### ATT&CK +* T1078 +* T1136 + +##### Kill Chain Phases + +###### CIS +* CIS 16 + +##### NIST +* PR.IP + +##### References + +### Asset Tracking +* id = 91c676cf-0b23-438d-abee-f6335e1fce77 +* date = 2017-09-13 +* version = 1 + +#### Description +Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. + +#### Narrative +This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. + +#### Detections +* Detect Unauthorized Assets by MAC address + +#### Data Models +* Network_Sessions + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 1 + +##### NIST +* PR.DS + +##### References +* https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ + +### Monitor Backup Solution +* id = abe807c7-1eb6-4304-ac32-6e7aacdb891d +* date = 2017-09-12 +* version = 1 + +#### Description +Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. + +#### Narrative +Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. + +#### Detections +* Extended Period Without Successful Netbackup Backups +* Unsuccessful Netbackup backups + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS +* CIS 10 + +##### NIST +* PR.IP + +##### References +* https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ + +### Monitor for Unauthorized Software +* id = 8892a655-6205-43f7-abba-06460e38c8ae +* date = 2017-09-15 +* version = 1 + +#### Description +Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. + +#### Narrative +It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ +It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. + +#### Detections +* Prohibited Software On Endpoint + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 2 + +##### NIST +* PR.DS + +##### References +* https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ + +### Monitor for Updates +* id = 9ef8d677-7b52-4213-a038-99cfc7acc2d8 +* date = 2017-09-15 +* version = 1 + +#### Description +Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. + +#### Narrative +It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ +Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ +Microsoft releases updates for Windows systems on a monthly cadence. They should be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed. + +#### Detections +* No Windows Updates in a time frame + +#### Data Models +* Updates + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS +* CIS 18 + +##### NIST +* PR.MA + +##### References +* https://learn.cisecurity.org/20-controls-download + +### Prohibited Traffic Allowed or Protocol Mismatch +* id = 6d13121c-90f3-446d-8ac3-27efbbc65218 +* date = 2017-09-11 +* version = 1 + +#### Description +Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. + +#### Narrative +A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. + +#### Detections +* Detect hosts connecting to dynamic domain providers +* Prohibited Network Traffic Allowed +* Protocol or Port Mismatch +* TOR Traffic + +#### Data Models +* Network_Resolution +* Network_Traffic + +#### Mappings + +##### ATT&CK +* T1043 +* T1048 + +##### Kill Chain Phases +* Actions on Objectives * Command and Control -* Exfiltration +* Delivery -###### Kill Chain Phases +###### CIS +* CIS 12 +* CIS 13 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* PR.AC + +##### References +* http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ + +### Router and Infrastructure Security +* id = 91c676cf-0b23-438d-abee-f6335e177e77 +* date = 2017-09-12 +* version = 1 + +#### Description +Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. + +#### Narrative +Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ +This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. + +#### Detections +* Detect New Login Attempts to Routers + +#### Data Models +* Authentication + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 11 + +##### NIST +* PR.IP + +##### References +* https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html +* https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html + +### Use of Cleartext Protocols +* id = 826e6431-aeef-41b4-9fc0-6d0985d65a21 +* date = 2017-09-15 +* version = 1 + +#### Description +Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. + +#### Narrative +Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. + +#### Detections +* Protocols passing authentication in cleartext + +#### Data Models +* Network_Traffic + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 14 + +##### NIST +* PR.DS + +##### References +* https://www.monkey.org/~dugsong/dsniff/ + + +## Cloud Security + +* [AWS Cross Account Activity](#AWS-Cross-Account-Activity) + +* [AWS Cryptomining](#AWS-Cryptomining) + +* [AWS Network ACL Activity](#AWS-Network-ACL-Activity) + +* [AWS Suspicious Provisioning Activities](#AWS-Suspicious-Provisioning-Activities) + +* [AWS User Monitoring](#AWS-User-Monitoring) + +* [Cloud Cryptomining](#Cloud-Cryptomining) + +* [Container Implantation Monitoring and Investigation](#Container-Implantation-Monitoring-and-Investigation) + +* [Kubernetes Scanning Activity](#Kubernetes-Scanning-Activity) + +* [Suspicious AWS EC2 Activities](#Suspicious-AWS-EC2-Activities) + +* [Suspicious AWS Login Activities](#Suspicious-AWS-Login-Activities) + +* [Suspicious AWS S3 Activities](#Suspicious-AWS-S3-Activities) + +* [Suspicious AWS Traffic](#Suspicious-AWS-Traffic) + +* [Unusual AWS EC2 Modifications](#Unusual-AWS-EC2-Modifications) + +### AWS Cross Account Activity +* id = 2f2f610a-d64d-48c2-b57c-967a2b49ab5a +* date = 2018-06-04 +* version = 1 + +#### Description +Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. + +#### Narrative +Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ +Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ +This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. + +#### Detections +* AWS Cross Account Activity From Previously Unseen Account + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 16 + +##### NIST +* DE.AE + +##### References +* https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ + +### AWS Cryptomining +* id = ced74200-8465-4bc3-bd2c-9a782eec6750 +* date = 2018-03-08 +* version = 1 + +#### Description +Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. + +#### Narrative +Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +hen malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So, it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ +This Analytic Story is focused on detecting suspicious new instances in your EC2 environment to help prevent such a disaster. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. + +#### Detections +* Abnormally High AWS Instances Launched by User +* EC2 Instance Started In Previously Unseen Region +* EC2 Instance Started With Previously Unseen AMI +* EC2 Instance Started With Previously Unseen Instance Type +* EC2 Instance Started With Previously Unseen User + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 1 +* CIS 12 +* CIS 13 + +##### NIST +* DE.AE +* DE.DP +* ID.AM + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +### AWS Network ACL Activity +* id = 2e8948a5-5239-406b-b56b-6c50ff268af4 +* date = 2018-05-21 +* version = 2 + +#### Description +Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. + +#### Narrative +AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. + +#### Detections +* AWS Network Access Control List Created with All Open Ports +* AWS Network Access Control List Deleted +* Detect Spike in Network ACL Activity +* Detect Spike in blocked Outbound Traffic from your AWS + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives * Command and Control +###### CIS +* CIS 11 +* CIS 12 + +##### NIST +* DE.AE +* DE.CM +* DE.DP +* PR.AC + +##### References +* https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html +* https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ + +### AWS Suspicious Provisioning Activities +* id = 3338b567-3804-4261-9889-cf0ca4753c7f +* date = 2018-03-16 +* version = 1 + +#### Description +Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. + +#### Narrative +Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ +This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. + +#### Detections +* AWS Cloud Provisioning From Previously Unseen City +* AWS Cloud Provisioning From Previously Unseen Country +* AWS Cloud Provisioning From Previously Unseen IP Address +* AWS Cloud Provisioning From Previously Unseen Region + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS +* CIS 1 + +##### NIST +* ID.AM + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +### AWS User Monitoring +* id = 2e8948a5-5239-406b-b56b-6c50f1269af3 +* date = 2018-03-12 +* version = 1 + +#### Description +Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. + +#### Narrative +It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ +Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ +The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. + +#### Detections +* Detect API activity from users without MFA +* Detect AWS API Activities From Unapproved Accounts +* Detect Spike in AWS API Activity +* Detect Spike in Security Group Activity +* Detect new API calls from user roles + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 1 +* CIS 16 + +##### NIST +* DE.CM +* DE.DP +* ID.AM +* PR.AC + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +* https://redlock.io/blog/cryptojacking-tesla + +### Cloud Cryptomining +* id = 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a +* date = 2019-10-02 +* version = 1 + +#### Description +Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. + +#### Narrative +Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ +This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. + +#### Detections +* Abnormally High AWS Instances Launched by User - MLTK +* Cloud Compute Instance Created By Previously Unseen User +* Cloud Compute Instance Created With Previously Unseen Image +* Cloud Compute Instance Created With Previously Unseen Instance Type +* Cloud Compute Instance Started In Previously Unused Region + +#### Data Models +* Cloud_Infrastructure + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 1 +* CIS 12 +* CIS 13 + +##### NIST +* DE.AE +* DE.DP +* ID.AM + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +### Container Implantation Monitoring and Investigation +* id = aa0e28b1-0521-4b6f-9d2a-7b87e34af246 +* date = 2020-02-20 +* version = 1 + +#### Description +Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. + +#### Narrative +Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. + +#### Detections +* GCP GCR container uploaded +* New container uploaded to AWS ECR + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS + +##### NIST + +##### References +* https://github.com/splunk/cloud-datamodel-security-research + +### Kubernetes Scanning Activity +* id = a9ef59cf-e981-4e66-9eef-bb049f695c09 +* date = 2020-04-15 +* version = 1 + +#### Description +This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. + +#### Narrative +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. + +#### Detections +* Amazon EKS Kubernetes Pod scan detection +* Amazon EKS Kubernetes cluster scan detection +* GCP Kubernetes cluster scan detection + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Reconnaissance + +###### CIS + +##### NIST + +##### References +* https://github.com/splunk/cloud-datamodel-security-research + +### Suspicious AWS EC2 Activities +* id = 2e8948a5-5239-406b-b56b-6c50f1268af3 +* date = 2018-02-09 +* version = 1 + +#### Description +Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. + +#### Narrative +AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. + +#### Detections +* Abnormally High AWS Instances Launched by User +* Abnormally High AWS Instances Launched by User - MLTK +* Abnormally High AWS Instances Terminated by User +* Abnormally High AWS Instances Terminated by User - MLTK +* EC2 Instance Started In Previously Unseen Region +* EC2 Instance Started With Previously Unseen User + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 1 +* CIS 12 +* CIS 13 + +##### NIST +* DE.AE +* DE.DP +* ID.AM + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + +### Suspicious AWS Login Activities +* id = 2e8948a5-5239-406b-b56b-6c59f1268af3 +* date = 2019-05-01 +* version = 1 + +#### Description +Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. + +#### Narrative +It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. + +#### Detections +* Detect AWS Console Login by User from New City +* Detect AWS Console Login by User from New Country +* Detect AWS Console Login by User from New Region +* Detect new user AWS Console Login + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 16 + +##### NIST +* DE.AE +* DE.DP + +##### References +* https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html + +### Suspicious AWS S3 Activities +* id = 2e8948a5-5239-406b-b56b-6c50w3168af3 +* date = 2018-07-24 +* version = 2 + +#### Description +Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. + +#### Narrative +As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ +Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ +Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. + +#### Detections +* Detect New Open S3 buckets +* Detect S3 access from a new IP +* Detect Spike in S3 Bucket deletion + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Actions on Objectives + +###### CIS +* CIS 13 +* CIS 14 + +##### NIST +* DE.CM +* DE.DP +* PR.AC +* PR.DS + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +* https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ + +### Suspicious AWS Traffic +* id = 2e8948a5-5239-406b-b56b-6c50f2168af3 +* date = 2018-05-07 +* version = 1 + +#### Description +Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). + +#### Narrative +A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ +Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ + Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ +The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. + +#### Detections +* Detect Spike in blocked Outbound Traffic from your AWS + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Command and Control + +###### CIS +* CIS 11 + +##### NIST +* PR.AC + +##### References +* https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ + +### Unusual AWS EC2 Modifications +* id = 73de57ef-0dfc-411f-b1e7-fa24428aeae0 +* date = 2018-04-09 +* version = 1 + +#### Description +Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. + +#### Narrative +A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ + Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. + +#### Detections +* EC2 Instance Modified With Previously Unseen User + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS +* CIS 1 + +##### NIST +* ID.AM + +##### References +* https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf + + +## Malware + +* [ColdRoot MacOS RAT](#ColdRoot-MacOS-RAT) + +* [DHS Report TA18-074A](#DHS-Report-TA18-074A) + +* [Dynamic DNS](#Dynamic-DNS) + +* [Emotet Malware DHS Report TA18-201A ](#Emotet-Malware--DHS-Report-TA18-201A-) + +* [Hidden Cobra Malware](#Hidden-Cobra-Malware) + +* [Orangeworm Attack Group](#Orangeworm-Attack-Group) + +* [Ransomware](#Ransomware) + +* [SamSam Ransomware](#SamSam-Ransomware) + +* [Unusual Processes](#Unusual-Processes) + +* [Windows File Extension and Association Abuse](#Windows-File-Extension-and-Association-Abuse) + +* [Windows Service Abuse](#Windows-Service-Abuse) + +### ColdRoot MacOS RAT +* id = bd91a2bc-d20b-4f44-a982-1bea98e86390 +* date = 2019-01-09 +* version = 1 + +#### Description +Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. + +#### Narrative +Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ +This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ +Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. + +#### Detections +* Osquery pack - ColdRoot detection +* Processes Tapping Keyboard Events + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Command and Control + +###### CIS +* CIS 4 +* CIS 8 + +##### NIST +* DE.DP +* PR.PT + +##### References +* https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ +* https://objective-see.com/blog/blog_0x2A.html +* https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ + +### DHS Report TA18-074A +* id = 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef +* date = 2020-01-22 +* version = 2 + +#### Description +Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. + +#### Narrative +The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ +There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ +One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ +Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. + +#### Detections +* Create local admin accounts using net exe +* Detect New Local Admin account +* Detect Outbound SMB Traffic +* Detect PsExec With accepteula Flag +* First time seen command line argument +* Malicious PowerShell Process - Execution Policy Bypass +* Processes launching netsh +* Registry Keys Used For Persistence +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Sc exe Manipulating Windows Services +* Scheduled Task Name Used by Dragonfly Threat Actors +* Single Letter Process On Endpoint +* Suspicious Reg exe Process + +#### Data Models +* Endpoint +* Network_Traffic + +#### Mappings + +##### ATT&CK +* T1031 +* T1043 +* T1050 +* T1053 +* T1059 +* T1064 +* T1078 +* T1086 +* T1089 +* T1103 +* T1112 +* T1131 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control +* Installation + +###### CIS +* CIS 12 +* CIS 16 +* CIS 2 +* CIS 3 +* CIS 5 +* CIS 7 +* CIS 8 + +##### NIST +* DE.AE +* DE.CM +* ID.AM +* PR.AC +* PR.AT +* PR.DS +* PR.IP +* PR.PT + +##### References +* https://www.us-cert.gov/ncas/alerts/TA18-074A + +### Dynamic DNS +* id = 8169f17b-ef68-4b59-aae8-586907301221 +* date = 2018-09-06 +* version = 2 + +#### Description +Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. + +#### Narrative +Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. + +#### Detections +* Detect hosts connecting to dynamic domain providers +* Detect web traffic to dynamic domain providers + +#### Data Models +* Network_Resolution +* Web + +#### Mappings + +##### ATT&CK +* T1041 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control + +###### CIS +* CIS 13 +* CIS 7 +* CIS 8 + +##### NIST +* DE.CM +* DE.DP +* PR.IP + +##### References +* https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html +* https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ +* http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ +* https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html + +### Emotet Malware DHS Report TA18-201A +* id = bb9f5ed2-916e-4364-bb6d-91c310efcf52 +* date = 2020-01-27 +* version = 1 + +#### Description +Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. + +#### Narrative +The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ +According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ +The searches in this Analytic Story will help you find executables that are rarely used in your environment, specific registry paths that malware often uses to ensure survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that Emotet or other malware has compromised your environment. + +#### Detections +* Detect Rare Executables +* Detect Use of cmd exe to Launch Script Interpreters +* Detection of tools built by NirSoft +* Email Attachments With Lots Of Spaces +* Prohibited Software On Endpoint +* Registry Keys Used For Persistence +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Suspicious Email Attachment Extensions + +#### Data Models +* Email +* Endpoint +* Network_Traffic + +#### Mappings + +##### ATT&CK +* T1043 +* T1059 +* T1072 +* T1087 +* T1103 +* T1131 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control +* Delivery +* Exploitation +* Installation + +###### CIS +* CIS 12 +* CIS 2 +* CIS 3 +* CIS 7 +* CIS 8 + +##### NIST +* DE.AE +* DE.CM +* ID.AM +* PR.DS +* PR.IP +* PR.PT + +##### References +* https://www.us-cert.gov/ncas/alerts/TA18-201A +* https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf +* https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html + +### Hidden Cobra Malware +* id = baf7580b-d4b4-4774-8173-7d198e9da335 +* date = 2020-01-22 +* version = 2 + +#### Description +Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. + +#### Narrative +North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ +These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ +In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ +Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. + +#### Detections +* Create or delete windows shares using net exe +* DNS Query Length Outliers - MLTK +* DNS Query Length With High Standard Deviation +* Detect Outbound SMB Traffic +* First time seen command line argument +* Remote Desktop Network Traffic +* Remote Desktop Process Running On System +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Suspicious File Write + +#### Data Models +* Endpoint +* Network_Resolution +* Network_Traffic + +#### Mappings + +##### ATT&CK +* T1043 +* T1059 +* T1064 +* T1076 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control + +###### CIS +* CIS 12 +* CIS 16 +* CIS 3 +* CIS 8 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* PR.AC +* PR.IP +* PR.PT + +##### References +* https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity +* https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf + +### Orangeworm Attack Group +* id = bb9f5ed2-916e-4364-bb6d-97c370efcf52 +* date = 2020-01-22 +* version = 2 + +#### Description +Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. + +#### Narrative +In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ +Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ +Although the group's motivation is unknown, its goal may be stealing patient information to sell on the black market. Another possible explanation is corporate espionage. \ +Healthcare may be a promising target, because it is notoriously behind in technology, often using older operating systems and neglecting to patch computers. Even so, the group was able to evade detection for a full three years. Sources say that the malware spread quickly within the target networks, infecting computers used to control medical devices, such as MRI and X-ray machines.\ +This Analytic Story is designed to help you detect and investigate suspicious activities that may be indicative of an Orangeworm attack. One detection search looks for command-line arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised. + +#### Detections +* First Time Seen Running Windows Service +* First time seen command line argument +* Sc exe Manipulating Windows Services + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1031 +* T1050 +* T1059 +* T1089 + +##### Kill Chain Phases +* Actions on Objectives +* Installation + +###### CIS +* CIS 2 +* CIS 3 +* CIS 5 +* CIS 8 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* ID.AM +* PR.AC +* PR.AT +* PR.DS +* PR.IP +* PR.PT + +##### References +* https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia +* https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ + +### Ransomware +* id = cf309d0d-d4aa-4fbb-963d-1e79febd3756 +* date = 2020-02-04 +* version = 1 + +#### Description +Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. + +#### Narrative +Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. + +#### Detections +* Common Ransomware Extensions +* Common Ransomware Notes +* Deleting Shadow Copies +* Prohibited Network Traffic Allowed +* Registry Keys Used For Persistence +* Remote Process Instantiation via WMI +* SMB Traffic Spike +* SMB Traffic Spike - MLTK +* Scheduled tasks used in BadRabbit ransomware +* Schtasks used for forcing a reboot +* Spike in File Writes +* Suspicious wevtutil Usage +* System Processes Run From Unexpected Locations +* TOR Traffic +* USN Journal Deletion +* Unusually Long Command Line +* Unusually Long Command Line - MLTK +* Windows Event Log Cleared + +#### Data Models +* Endpoint +* Network_Traffic + +#### Mappings + +##### ATT&CK +* T1036 +* T1043 +* T1047 +* T1048 +* T1053 +* T1070 +* T1103 +* T1131 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control +* Delivery + +###### CIS +* CIS 10 +* CIS 12 +* CIS 3 +* CIS 5 +* CIS 6 +* CIS 8 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* DE.DP +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +##### References +* https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ +* https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html + +### SamSam Ransomware +* id = c4b89506-fbcf-4cb7-bfd6-527e54789604 +* date = 2018-12-13 +* version = 1 + +#### Description +Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. + +#### Narrative +The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ +Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ +SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ +In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ +According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ +This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. + +#### Detections +* Batch File Write to System32 +* Common Ransomware Extensions +* Common Ransomware Notes +* Deleting Shadow Copies +* Detect PsExec With accepteula Flag +* Detect attackers scanning for vulnerable JBoss servers +* Detect malicious requests to exploit JBoss servers +* File with Samsam Extension +* Prohibited Software On Endpoint +* Remote Desktop Network Bruteforce +* Remote Desktop Network Traffic +* Samsam Test File Write +* Spike in File Writes + +#### Data Models +* Endpoint +* Network_Traffic +* Web + +#### Mappings + +##### ATT&CK +* T1059 +* T1076 +* T1082 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control +* Delivery +* Installation +* Reconnaissance + +###### CIS +* CIS 10 +* CIS 12 +* CIS 16 +* CIS 18 +* CIS 2 +* CIS 3 +* CIS 4 +* CIS 8 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* ID.AM +* ID.RA +* PR.AC +* PR.DS +* PR.IP +* PR.MA +* PR.PT + +##### References +* https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ +* https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ +* https://thehackernews.com/2018/07/samsam-ransomware-attacks.html + +### Unusual Processes +* id = f4368e3f-d59f-4192-84f6-748ac5a3ddb6 +* date = 2020-02-04 +* version = 2 + +#### Description +Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. + +#### Narrative +Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ +This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ +In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. + +#### Detections +* Detect Rare Executables +* Detect processes used for System Network Configuration Discovery +* RunDLL Loading DLL By Ordinal +* System Processes Run From Unexpected Locations +* Uncommon Processes On Endpoint +* Unusually Long Command Line +* Unusually Long Command Line - MLTK + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1015 +* T1036 +* T1085 + +##### Kill Chain Phases +* Actions on Objectives +* Command and Control +* Installation + +###### CIS +* CIS 2 +* CIS 8 + +##### NIST +* DE.CM +* ID.AM +* PR.DS +* PR.PT + +##### References +* https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html +* https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf +* https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 + +### Windows File Extension and Association Abuse +* id = 30552a76-ac78-48e4-b3c0-de4e34e9563d +* date = 2018-01-26 +* version = 1 + +#### Description +Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. + +#### Narrative +Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ + Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ + Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ +Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ +Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. + +#### Detections +* Execution of File With Spaces Before Extension +* Execution of File with Multiple Extensions +* Suspicious Changes to File Associations + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1042 + +##### Kill Chain Phases +* Actions on Objectives + ###### CIS * CIS 3 * CIS 8 -* CIS 12 -###### NIST +##### NIST +* DE.CM * PR.IP * PR.PT -* PR.AC -* DE.AE -* DE.CM - -##### Maintainers -* name = David Dorsey -* email = davidd@splunk.com -* company = Splunk ##### References -* https://attack.mitre.org/tactics/TA0004/ +* https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ +* https://attack.mitre.org/wiki/Technique/T1042 + +### Windows Service Abuse +* id = 6dbd810e-f66d-414b-8dfc-e46de55cbfe2 +* date = 2017-11-02 +* version = 3 + +#### Description +Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. + +#### Narrative +The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. + +#### Detections +* First Time Seen Running Windows Service +* Reg exe Manipulating Windows Services Registry Keys +* Sc exe Manipulating Windows Services + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1031 +* T1050 +* T1089 + +##### Kill Chain Phases +* Actions on Objectives +* Installation + +###### CIS +* CIS 3 +* CIS 5 +* CIS 8 +* CIS 9 + +##### NIST +* DE.AE +* DE.CM +* PR.AC +* PR.AT +* PR.IP +* PR.PT + +##### References +* https://attack.mitre.org/wiki/Technique/T1050 +* https://attack.mitre.org/wiki/Technique/T1031 + + +## Vulnerability + +* [Apache Struts Vulnerability](#Apache-Struts-Vulnerability) + +* [JBoss Vulnerability](#JBoss-Vulnerability) + +* [Spectre And Meltdown Vulnerabilities](#Spectre-And-Meltdown-Vulnerabilities) + +* [Splunk Enterprise Vulnerability](#Splunk-Enterprise-Vulnerability) + +* [Splunk Enterprise Vulnerability CVE-2018-11409](#Splunk-Enterprise-Vulnerability-CVE-2018-11409) + +### Apache Struts Vulnerability +* id = 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e +* date = 2018-12-06 +* version = 1 + +#### Description +Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. + +#### Narrative +In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ +The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ +This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ +The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ +First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ +hen looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ +Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ +Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ +Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ +hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ +hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ +In the event that a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that have the file open, what processes created and/or modified the file, and the number of systems that may have this file can help to determine if the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes quickly help determine whether it is malicious in nature.\ +Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, there may be activity initiated via a compromised website the user visited.\ +It can also be very helpful to examine various behaviors of the process of interest or the parent of the process that is of interest. For example, if it turns out that the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might also be worth further scrutiny. If a process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. + +#### Detections +* Suspicious Java Classes +* Unusually Long Content-Type Length +* Web Servers Executing Suspicious Processes + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1082 + +##### Kill Chain Phases +* Actions on Objectives +* Delivery +* Exploitation + +###### CIS +* CIS 12 +* CIS 18 +* CIS 3 +* CIS 4 + +##### NIST +* DE.AE +* DE.CM +* ID.RA +* PR.IP +* PR.MA +* PR.PT +* RS.MI + +##### References +* https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf + +### JBoss Vulnerability +* id = 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd +* date = 2017-09-14 +* version = 1 + +#### Description +In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. + +#### Narrative +This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ +It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ +hen looking at the target of the behavior uncovered by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to identify other recent events involving the target. This can help tie different events together and give further situational awareness regarding the target host.\ +Various types of information for external systems should be reviewed and, potentially, collected if the incident is, indeed, judged to be malicious. This data may be useful for generating your own threat intelligence, so you can create future alerts.\ +The following factors may assist you in determining whether the event is malicious: \ +1. Country of origin\ +1. Responsible party\ +1. Fully qualified domain names associated with the external IP address\ +1. Registration of fully qualified domain names associated with external IP address Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you qualify and understand the event and possible motivation for the attack. In addition, there are various sources that may provide reputation information on the IP address or domain name, which can assist you in determining whether the event is malicious in nature. Finally, determining whether there are other events associated with the IP address may help connect data points or expose other historic events that might be brought back into scope.\ +Gathering various data on the system of interest can sometimes help quickly determine whether something suspicious is happening. Some of these items include determining who else may have logged into the system recently, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and/or whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ +hen a specific service or application is targeted, it is often helpful to know the associated version, to help determine whether it is vulnerable to a specific exploit.\ +If you suspect an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ +If a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that opened the file, the processes that may have created and/or modified the file, and how many other systems potentially have this file can you determine whether the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes help you quickly determine if it is malicious in nature.\ +Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if svchost.exe is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. \ +It can also be helpful to examine various behaviors of and the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see whether the parent process spawned other processes that might also warrant further scrutiny. If a process is suspect, a review of the network connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. + +#### Detections +* Detect attackers scanning for vulnerable JBoss servers +* Detect malicious requests to exploit JBoss servers + +#### Data Models +* Web + +#### Mappings + +##### ATT&CK +* T1082 + +##### Kill Chain Phases +* Delivery +* Reconnaissance + +###### CIS +* CIS 18 + +##### NIST +* DE.CM + +##### References +* http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html + +### Spectre And Meltdown Vulnerabilities +* id = 6d3306f6-bb2b-4219-8609-8efad64032f2 +* date = 2018-01-08 +* version = 1 + +#### Description +Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. + +#### Narrative +Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. + +#### Detections +* Spectre and Meltdown Vulnerable Systems + +#### Data Models +* Vulnerabilities + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases + +###### CIS +* CIS 4 + +##### NIST +* DE.CM + +##### References +* https://meltdownattack.com/ + +### Splunk Enterprise Vulnerability +* id = 4e692b96-de2d-4bd1-9105-37e2368a8db1 +* date = 2017-09-19 +* version = 1 + +#### Description +Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. + +#### Narrative +This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ +\ +1. Splunk Enterprise 6.4.x, prior to 6.4.3\ +1. Splunk Enterprise 6.3.x, prior to 6.3.6\ +1. Splunk Enterprise 6.2.x, prior to 6.2.10\ +1. Splunk Enterprise 6.1.x, prior to 6.1.11\ +1. Splunk Enterprise 6.0.x, prior to 6.0.12\ +1. Splunk Enterprise 5.0.x, prior to 5.0.16\ +1. Splunk Light, prior to 6.4.3CVE-2016-4859 allows attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. (Credit: Noriaki Iwasaki, Cyber Defense Institute, Inc.).\ +It is important to ensure that your Splunk deployment is being kept up to date and is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites. + +#### Detections +* Open Redirect in Splunk Web + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Delivery + +###### CIS +* CIS 18 + +##### NIST +* DE.CM + +##### References +* http://www.splunk.com/view/SP-CAAAPQ6#announce +* https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 + +### Splunk Enterprise Vulnerability CVE-2018-11409 +* id = 1fc34cbc-34e9-43ba-87ab-6811c9e95400 +* date = 2018-06-14 +* version = 1 + +#### Description +Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. + +#### Narrative +Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ +Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ +Read more in Splunk's official response: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings.\ +A detection search within this Analytic Story looks for vulnerabilities described in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip. + +#### Detections +* Splunk Enterprise Information Disclosure + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Delivery + +###### CIS +* CIS 18 + +##### NIST +* DE.CM + +##### References +* https://nvd.nist.gov/vuln/detail/CVE-2018-11409 +* https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings +* https://www.exploit-db.com/exploits/44865/ + + diff --git a/docs/story.spec.json b/docs/story.spec.json deleted file mode 100644 index 7eda0fa321..0000000000 --- a/docs/story.spec.json +++ /dev/null @@ -1,271 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/story.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "description": "The fields that make up the manifest of a version 2 Analytic Story", - "meta:license": [ - "This file is licensed to you under the Apache License, Version 2.0 (the 'License');", - "you may not use this file except in compliance with the License. You may obtain a copy", - "of the License at http://www.apache.org/licenses/LICENSE-2.0" - ], - "properties": { - "category": { - "description": "The category to which the Analytic Story belongs", - "items": { - "enum": [ - "Abuse", - "Account Compromise", - "Account Sharing", - "Adversary Tactics", - "Best Practices", - "Cloud Security", - "Command and Control", - "Compliance", - "Data Exfiltration", - "Denial of Service", - "Endpoint Compromise", - "GDPR", - "IAM Analytics", - "Insider Threat", - "Lateral Movement", - "Malware", - "Network Attack", - "Operations", - "Other", - "Privilege Escalation", - "Phishing", - "Ransomware", - "SaaS", - "Scanning", - "Shadow IT", - "Threat Intelligence", - "Unauthorized Software", - "Vulnerability", - "Web Attack" - ], - "examples": [ - "Adversary Tactics" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Content Updates are in the ESCU channel", - "examples": [ - "ESCU" - ], - "type": "string" - }, - "creation_date": { - "description": "The date this story was created", - "examples": [ - "2017-11-21" - ], - "type": "string" - }, - "description": { - "description": "A high-level description or goal of the Analytic Story", - "examples": [ - "Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records." - ], - "type": "string" - }, - "detections": { - "description": "An array of detection mechanisms from Splunk, UBA, and Phantom. ", - "items": { - "additionalProperties": false, - "properties": { - "detection_id": { - "description": "Unique identifier of the detection, in the form of UUID", - "examples": [ - "1169w17b-ef78-4b59-aae8-5369073014e1" - ], - "type": "string" - }, - "name": { - "description": "Name of the Detection. This can be a name of a Splunk correlation search, a UBA threat, or a Phantom detection playbook.", - "examples": [ - "DNS record changed" - ], - "type": "string" - }, - "type": { - "description": "Product providing the detection", - "enum": [ - "splunk", - "uba", - "phantom" - ], - "examples": [ - "splunk", - "uba", - "phantom" - ], - "type": "string" - } - }, - "required": [ - "detection_id", - "type", - "name" - ], - "type": "object" - }, - "type": "array" - }, - "id": { - "description": "A unique identifier for the Analytic Story", - "examples": [ - "8169f17b-ef68-4b59-aae8-5869073014e1" - ], - "format": "uuid", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person maintaining this Analytic Story", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this Analytic Story", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this Analytic Story", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to this Analytic Story", - "examples": [ - "2018-09-06" - ], - "type": "string" - }, - "name": { - "description": "The name of the Analytic Story", - "examples": [ - "DNS Hijacking" - ], - "type": "string" - }, - "narrative": { - "description": "Long-form text that describes the Analytic Story and the rationale behind it, as well as an overview of the included searches, and how they enable the story", - "examples": [ - "Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.

The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous." - ], - "type": "string" - }, - "original_authors": { - "description": "An array of the original authors of the Analytic Story", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person who originally authored the Analytic Story", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the Analytic Story", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the Analytic Story", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "references": { - "description": "An array of URLs that provide information about the problem the story addresses", - "examples": [ - "https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "spec_version": { - "description": "The version of the Analytic Story specification this manifest follows", - "examples": [ - 2 - ], - "type": "integer" - }, - "usecase": { - "description": "The use case to which the Analytic Story belongs", - "enum": [ - "Security Monitoring", - "Advanced Threat Detection", - "Insider Threat", - "Compliance", - "Fraud Detection" - ], - "examples": [ - "Advanced Threat Detection" - ] - }, - "version": { - "description": "The version of the Analytic Story (bumps the version number when the story gets an update)", - "examples": [ - "1.0" - ], - "type": "string" - } - }, - "required": [ - "id", - "category", - "usecase", - "name", - "description", - "detections", - "version" - ], - "title": "Analytic Story Manifest", - "type": "object" -} diff --git a/docs/story.spec.md b/docs/story.spec.md deleted file mode 100644 index 8c575c4352..0000000000 --- a/docs/story.spec.md +++ /dev/null @@ -1,725 +0,0 @@ - -# Analytic Story Manifest Schema - -``` -https://api.splunkresearch.com/schemas/story.json -``` - -The fields that make up the manifest of a version 2 Analytic Story - -| Abstract | Extensible | Status | Identifiable | Custom Properties | Additional Properties | Defined In | -|----------|------------|--------|--------------|-------------------|-----------------------|------------| -| Can be instantiated | No | Experimental | No | Forbidden | Permitted | | - -# Analytic Story Manifest Properties - -| Property | Type | Required | Nullable | Defined by | -|----------|------|----------|----------|------------| -| [category](#category) | `enum[]` | **Required** | No | Analytic Story Manifest (this schema) | -| [channel](#channel) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [creation_date](#creation_date) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [description](#description) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [detections](#detections) | `object[]` | **Required** | No | Analytic Story Manifest (this schema) | -| [id](#id) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [maintainers](#maintainers) | `object[]` | Optional | No | Analytic Story Manifest (this schema) | -| [modification_date](#modification_date) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [name](#name) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| [narrative](#narrative) | `string` | Optional | No | Analytic Story Manifest (this schema) | -| [original_authors](#original_authors) | `object[]` | Optional | No | Analytic Story Manifest (this schema) | -| [references](#references) | `string[]` | Optional | No | Analytic Story Manifest (this schema) | -| [spec_version](#spec_version) | `integer` | Optional | No | Analytic Story Manifest (this schema) | -| [usecase](#usecase) | `enum` | **Required** | No | Analytic Story Manifest (this schema) | -| [version](#version) | `string` | **Required** | No | Analytic Story Manifest (this schema) | -| `*` | any | Additional | Yes | this schema *allows* additional properties | - -## category - -The category to which the Analytic Story belongs - -`category` - -* is **required** -* type: `enum[]` -* between `0` and `1` items in the array -* defined in this schema - -### category Type - - -Array type: `enum[]` - -All items must be of the type: -`string` - - - - - - - - - - -## channel - -A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Content Updates are in the ESCU channel - -`channel` - -* is optional -* type: `string` -* defined in this schema - -### channel Type - - -`string` - - - - - - -### channel Example - -```json -"ESCU" -``` - - -## creation_date - -The date this story was created - -`creation_date` - -* is optional -* type: `string` -* defined in this schema - -### creation_date Type - - -`string` - - - - - - -### creation_date Example - -```json -"2017-11-21" -``` - - -## description - -A high-level description or goal of the Analytic Story - -`description` - -* is **required** -* type: `string` -* defined in this schema - -### description Type - - -`string` - - - - - - -### description Example - -```json -"Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records." -``` - - -## detections - -An array of detection mechanisms from Splunk, UBA and phantom. - -`detections` - -* is **required** -* type: `object[]` -* defined in this schema - -### detections Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `detection_id`| string | **Required** | -| `name`| string | **Required** | -| `type`| string | **Required** | - - - -#### detection_id - -unique identifier of the detection, in the form of UUID - -`detection_id` - -* is **required** -* type: `string` - -##### detection_id Type - - -`string` - - - - - - -##### detection_id Example - -```json -1169w17b-ef78-4b59-aae8-5369073014e1 -``` - - - - -#### name - -Name of the Detection. It can be a name of a Splunk correlation search name, a UBA threat or a Phantom detection playbook. - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -DNS record changed -``` - - - - -#### type - -What product gives you a detection - -`type` - -* is **required** -* type: `enum` - -The value of this property **must** be equal to one of the [known values below](#detections-known-values). - -##### type Known Values -| Value | Description | -|-------|-------------| -| `splunk` | | -| `uba` | | -| `phantom` | | - - - -##### type Examples - -```json -splunk -``` - -```json -uba -``` - -```json -phantom -``` - - - - - - - - - - -## id - -A unique identifier for the Analytic Story - -`id` - -* is **required** -* type: `string` -* defined in this schema - -### id Type - - -`string` - - - - - - -### id Example - -```json -"8169f17b-ef68-4b59-aae8-5869073014e1" -``` - - -## maintainers - -An array of the current maintainers of the Analytic Story. - -`maintainers` - -* is optional -* type: `object[]` -* defined in this schema - -### maintainers Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person maintaining this Analytic Story - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person maintaining this Analytic Story - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person maintaining this Analytic Story - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## modification_date - -The date of the most recent modification to this Analytic Story - -`modification_date` - -* is optional -* type: `string` -* defined in this schema - -### modification_date Type - - -`string` - - - - - - -### modification_date Example - -```json -"2018-09-06" -``` - - -## name - -The name of the Analytic Story - -`name` - -* is **required** -* type: `string` -* defined in this schema - -### name Type - - -`string` - - - - - - -### name Example - -```json -"DNS Hijacking" -``` - - -## narrative - -Long-form text that describes the Analytic Story and the rationale behind it, as well as an overview of the included searches, and how they enable the story - -`narrative` - -* is optional -* type: `string` -* defined in this schema - -### narrative Type - - -`string` - - - - - - -### narrative Example - -```json -"Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.

The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous." -``` - - -## original_authors - -An array of the original authors of the Analytic Story - -`original_authors` - -* is optional -* type: `object[]` -* defined in this schema - -### original_authors Type - - -Array type: `object[]` - -All items must be of the type: -`object` with following properties: - - -| Property | Type | Required | -|----------|------|----------| -| `company`| string | **Required** | -| `email`| string | **Required** | -| `name`| string | **Required** | - - - -#### company - -Company associated with the person who originally authored the Analytic Story - -`company` - -* is **required** -* type: `string` - -##### company Type - - -`string` - - - - - - -##### company Example - -```json -Splunk -``` - - - - -#### email - -Email address of the person who originally authored the Analytic Story - -`email` - -* is **required** -* type: `string` - -##### email Type - - -`string` - - - - - - -##### email Example - -```json -daftpunk@splunk.com -``` - - - - -#### name - -Name of the person who originally authored the Analytic Story - -`name` - -* is **required** -* type: `string` - -##### name Type - - -`string` - - - - - - -##### name Example - -```json -Daft Punk -``` - - - - - - - - - -## references - -An array of URLs that give information about the problem the story is addressing - -`references` - -* is optional -* type: `string[]` -* at least `0` items in the array -* defined in this schema - -### references Type - - -Array type: `string[]` - -All items must be of the type: -`string` - - - - - - - - -### references Example - -```json -"https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" -``` - - -## spec_version - -The version of the Analytic Story specification this manifest follows - -`spec_version` - -* is optional -* type: `integer` -* defined in this schema - -### spec_version Type - - -`integer` - - - - - - -### spec_version Example - -```json -2 -``` - - -## usecase - -The usecase to which the Analytic Story belongs to. - -`usecase` - -* is **required** -* type: `enum` -* defined in this schema - -The value of this property **must** be equal to one of the [known values below](#usecase-known-values). - -### usecase Known Values -| Value | Description | -|-------|-------------| -| `Security Monitoring` | | -| `Advanced Threat Detection` | | -| `Insider Threat` | | -| `Compliance` | | -| `Fraud Detection` | | - - - -### usecase Example - -```json -"Advanced Threat Detection" -``` - - -## version - -The version of the Analytic Story, bump the version number when the story gets an update - -`version` - -* is **required** -* type: `string` -* defined in this schema - -### version Type - - -`string` - - - - - - -### version Example - -```json -"1.0" -``` - diff --git a/investigations/aws_activity_via_region.yml b/investigations/aws_activity_via_region.yml deleted file mode 100644 index 228f66bcb0..0000000000 --- a/investigations/aws_activity_via_region.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2018-02-09' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search lists all the user activities logged by CloudTrail for a - specific region in question and will create a table of the values of parameters - requested, the type of the event and the response from the AWS API by each user -entities: - - awsRegion -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 -investigate: - splunk: - fields_required: - - awsRegion - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail awsRegion={awsRegion}| rename requestParameters.instancesSet.items{}.instanceId - as instanceId| stats values(eventName) by userName instanceId' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-02-09' -name: Investigate AWS activities via region name -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/aws_network_interface_details.yml b/investigations/aws_network_interface_details.yml deleted file mode 100644 index 76b0093d91..0000000000 --- a/investigations/aws_network_interface_details.yml +++ /dev/null @@ -1,42 +0,0 @@ -creation_date: '2018-05-07' -data_metadata: - data_source: - - AWS Configuration Logs - data_sourcetypes: - - aws:config - providing_technologies: - - AWS -description: This search queries AWS configuration logs and returns the information - about a specific network interface via network interface ID. The information will - include the ARN of the network interface, its relationships with other AWS resources, - the public and the private IP associated with the network interface. -entities: - - resourceId -how_to_implement: In order to implement this search, you must install the AWS App - for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) - and configure your AWS configuration inputs -id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241 -investigate: - splunk: - fields_required: - - resourceId - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '0' - search: '| search sourcetype=aws:config resourceId={resourceId} | table _time - ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress - configuration.privateIpAddresses{}.association.publicIp' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-07' -name: AWS Network Interface details via resourceId -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/aws_s3_bucket_details.yml b/investigations/aws_s3_bucket_details.yml deleted file mode 100644 index f2c6117727..0000000000 --- a/investigations/aws_s3_bucket_details.yml +++ /dev/null @@ -1,42 +0,0 @@ -creation_date: '2018-06-26' -data_metadata: - data_source: - - AWS Configuration Logs - data_sourcetypes: - - aws:config - providing_technologies: - - AWS -description: This search queries AWS configuration logs and returns the information - about a specific S3 bucket. The information returned includes the time the S3 bucket - was created, the resource ID, the region it belongs to, the value of action performed, - AWS account ID, and configuration values of the access-control lists associated - with the bucket. -entities: - - bucketName -how_to_implement: To implement this search, you must install the AWS App for Splunk - (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and - configure your AWS inputs. -id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242 -investigate: - splunk: - fields_required: - - bucketName - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '0' - search: '| search sourcetype=aws:config resourceId={bucketName} | table resourceCreationTime - resourceId awsRegion action aws_account_id supplementaryConfiguration.AccessControlList' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-06-26' -name: AWS S3 Bucket details via bucketName -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/aws_user_activities_by_user_field.yml b/investigations/aws_user_activities_by_user_field.yml deleted file mode 100644 index b80adfa1f4..0000000000 --- a/investigations/aws_user_activities_by_user_field.yml +++ /dev/null @@ -1,42 +0,0 @@ -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search lists all the logged CloudTrail activities by a specific - user and will create a table containing the source of the user, the region of the - activity, the name and type of the event, the action taken, and the user's identity - information. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 -investigate: - splunk: - fields_required: - - user - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail user={user} | table _time userIdentity.type - userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName - eventType ' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-03-12' -name: Investigate AWS User Activities by user field -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/aws_user_activity_by_accesskeyid.yml b/investigations/aws_user_activity_by_accesskeyid.yml deleted file mode 100644 index 214f208838..0000000000 --- a/investigations/aws_user_activity_by_accesskeyid.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2018-06-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves the times, ARN, source IPs, AWS regions, event - names, and the result of the event for specific credentials. -entities: - - accessKeyId -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: 703b65a4-a0ae-4171-965d-45507506c64f -investigate: - splunk: - fields_required: - - accessKeyId - schedule: - cron_schedule: '' - earliest_time: '0' - latest_time: '14400' - search: '| search sourcetype=aws:cloudtrail userIdentity.accessKeyId={accessKeyId} - | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip - | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-08' -name: AWS Investigate User Activities By AccessKeyId -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/aws_user_activity_by_src_user.yml b/investigations/aws_user_activity_by_src_user.yml deleted file mode 100644 index da5b6349de..0000000000 --- a/investigations/aws_user_activity_by_src_user.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2018-06-08' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves the times, ARN, source IPs, AWS regions, event - names, and the result of the event for specific ARNs. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: b0d2e6a8-75fa-4b1b-9486-3d32acadf891 -investigate: - splunk: - fields_required: - - src_user - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail userIdentity.arn={src_user} | spath - output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table - _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-06-08' -name: AWS Investigate User Activities By Source User -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/container_listing_aws_investigation.yml b/investigations/container_listing_aws_investigation.yml deleted file mode 100644 index 0ce5b1c5a9..0000000000 --- a/investigations/container_listing_aws_investigation.yml +++ /dev/null @@ -1,35 +0,0 @@ -creation_date: '2020-02-20' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - AWS CloudTrail logs - providing_technologies: - - AWS -description: This search lists all the users performing a list image operation on AWS Elastic Container Registry. Listing source user, image id, source IP, user type, http user agent. This search also gives counts of unique user agents per listing source. -entities: - - src_user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.You must also install Cloud Infrastructure Data Model. -id: 6027623f-7d10-4847-af3b-8d7e87970451 -investigate: - splunk: - fields_required: - - Compute.event_name - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count > 1' -maintainers: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -modification_date: '2020-02-20' -name: Investigate AWS ECR container listing activity -original_authors: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/dns_server_history.yml b/investigations/dns_server_history.yml deleted file mode 100644 index f70538a61c..0000000000 --- a/investigations/dns_server_history.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2017-04-10' -data_metadata: - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: While investigating any detections it is important to understand which - and how many DNS servers a host has connected to in the past. This search uses data - that is tagged as DNS and gives you a count and list of DNS servers that a particular - host has connected to the previous 24 hours. -entities: - - src_ip -how_to_implement: To successfully implement this search, you must be ingesting your - DNS traffic -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 -investigate: - splunk: - fields_required: - - src_ip - schedule: - cron_schedule: '' - earliest_time: '0' - latest_time: '86400' - search: '| search tag=dns src_ip={src_ip} dest_port=53 | streamstats time_window=1d - count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count - | sort -count' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-11-09' -name: Get DNS Server History for a host -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/dns_traffic_stats.yml b/investigations/dns_traffic_stats.yml deleted file mode 100644 index dc3d435032..0000000000 --- a/investigations/dns_traffic_stats.yml +++ /dev/null @@ -1,45 +0,0 @@ -creation_date: '2017-04-10' -data_metadata: - data_models: - - Network_Traffic - data_source: - - DNS - providing_technologies: - - Splunk Stream - - Bro -description: 'This search calculates the ratio of DNS traffic originating and coming - from a host to a list of DNS servers over the last 24 hours. A high value of this - ratio could be very useful to quickly understand if a src_ip (host) is sending a - high volume of data out via port 53, could be an indicator of data exfiltration - via DNS. ' -entities: - - src_ip - - dest_ip -how_to_implement: You must be ingesting your network traffic -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 -investigate: - splunk: - fields_required: - - src_ip - - dest_ip - schedule: - cron_schedule: '' - earliest_time: '0' - latest_time: '86400' - search: '| tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" - sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where - nodename=All_Traffic All_Traffic.dest_port=53 All_Traffic.src={src_ip} All_Traffic.dest={dest_ip} - | eval ratio = (bytes_out/bytes_in) | table ratio' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-11-09' -name: Get DNS traffic ratio -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/email_sent_to_hidden_cobra_actors.yml b/investigations/email_sent_to_hidden_cobra_actors.yml deleted file mode 100644 index f62814d94e..0000000000 --- a/investigations/email_sent_to_hidden_cobra_actors.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-06-14' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: 'This search returns the information of the users that sent emails to - the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, - and from `redhat@gmail.com`.' -entities: - - src_user - - recipient -how_to_implement: To successfully implement this search you must ingest your email - logs or capture unencrypted email communications within network traffic, and populate - the Email data model. -id: 5df39b3f-347d-4869-b673-8r45ad4616fe -investigate: - splunk: - fields_required: - - src_user - - recipient - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '0' - search: '| from datamodel Email.All_Email | search recipient=misswang8107@gmail.com - OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time) - as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-06-14' -name: Get Outbound Emails to Hidden Cobra Threat Actors -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/first_occurrence_mac_address.yml b/investigations/first_occurrence_mac_address.yml deleted file mode 100644 index 60338ef9c0..0000000000 --- a/investigations/first_occurrence_mac_address.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2017-06-14' -data_metadata: - data_models: - - Network_Sessions - data_source: - - IP Address Assignment - providing_technologies: - - Splunk Stream - - Bro - - Microsoft Windows -description: This search allows you to gather more context around a notable which - has detected a new device connecting to your network. Use this search to determine - the first and last occurrences of the suspicious device attempting to connect with - your network. -entities: - - src_mac -how_to_implement: To successfully implement this search, you must be ingesting the - logs from your DHCP server. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd33 -investigate: - splunk: - fields_required: - - src_mac - schedule: - cron_schedule: '' - earliest_time: '864000' - latest_time: '86400' - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) - as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP - All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= {src_mac} - by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-13' -name: Get First Occurrence and Last Occurrence of a MAC Address -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_acl_details.yml b/investigations/get_acl_details.yml deleted file mode 100644 index 95b4f4bc0d..0000000000 --- a/investigations/get_acl_details.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2018-01-18' -data_metadata: - data_source: - - AWS Description - data_sourcetypes: - - aws:description - providing_technologies: - - AWS -description: This search queries AWS description logs and returns all the information - about a specific network ACL via network ACL ID -entities: - - networkAclId -how_to_implement: In order to implement this search, you must install the AWS App - for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) - and configure your AWS description inputs. -id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242 -investigate: - splunk: - fields_required: - - networkAclId - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '0' - search: '| search sourcetype=aws:description id={networkAclId} | table id account_id - vpc_id network_acl_entries{}.*' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-01-22' -name: AWS Network ACL Details from ID -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_all_aws_activitiy_from_city.yml b/investigations/get_all_aws_activitiy_from_city.yml deleted file mode 100644 index 4ae77e68f7..0000000000 --- a/investigations/get_all_aws_activitiy_from_city.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-03-19' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves all the activity from a specific city and will - create a table containing the time, city, ARN, username, the type of user, the source - IP address, the AWS region the activity was in, the API called, and whether or not - the API call was successful. -entities: - - City -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: 0abeeb40-1255-4b68-91d1-7a7eb410c4b8 -investigate: - splunk: - fields_required: - - City - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search - City={City} | spath output=user path=userIdentity.arn | spath output=awsUserName - path=userIdentity.userName | spath output=userType path=userIdentity.type | - rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, - src_ip, awsRegion, eventName, errorCode' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-19' -name: Get All AWS Activity From City -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_all_aws_activitiy_from_country.yml b/investigations/get_all_aws_activitiy_from_country.yml deleted file mode 100644 index 556e55c8dc..0000000000 --- a/investigations/get_all_aws_activitiy_from_country.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-03-19' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves all the activity from a specific country and will - create a table containing the time, country, ARN, username, the type of user, the - source IP address, the AWS region the activity was in, the API called, and whether - or not the API call was successful. -entities: - - Country -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: e763cdb9-00da-41e0-9bda-444debc9501a -investigate: - splunk: - fields_required: - - Country - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search - Country={Country} | spath output=user path=userIdentity.arn | spath output=awsUserName - path=userIdentity.userName | spath output=userType path=userIdentity.type | - rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, - src_ip, awsRegion, eventName, errorCode' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-19' -name: Get All AWS Activity From Country -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_all_aws_activitiy_from_region.yml b/investigations/get_all_aws_activitiy_from_region.yml deleted file mode 100644 index 09317efe01..0000000000 --- a/investigations/get_all_aws_activitiy_from_region.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-03-19' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves all the activity from a specific geographic region - and will create a table containing the time, geographic region, ARN, username, the - type of user, the source IP address, the AWS region the activity was in, the API - called, and whether or not the API call was successful. -entities: - - Region -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: 5b794bef-1743-4f6f-804a-43915a2702ff -investigate: - splunk: - fields_required: - - Region - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search - Region={Region} | spath output=user path=userIdentity.arn | spath output=awsUserName - path=userIdentity.userName | spath output=userType path=userIdentity.type | - rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, - src_ip, awsRegion, eventName, errorCode' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-19' -name: Get All AWS Activity From Region -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_all_aws_activitiy_from_src_ip.yml b/investigations/get_all_aws_activitiy_from_src_ip.yml deleted file mode 100644 index c91728bf7d..0000000000 --- a/investigations/get_all_aws_activitiy_from_src_ip.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-03-19' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search retrieves all the activity from a specific IP address and - will create a table containing the time, ARN, username, the type of user, the IP - address, the AWS region the activity was in, the API called, and whether or not - the API call was successful. -entities: - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: 446ec87a-85c6-40d4-b060-bea4498281d6 -investigate: - splunk: - fields_required: - - src_ip - schedule: - cron_schedule: '' - earliest_time: '14400' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search - sourceIPAddress={src_ip} | spath output=user path=userIdentity.arn | spath output=awsUserName - path=userIdentity.userName | spath output=userType path=userIdentity.type | - rename sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, - awsRegion, eventName, errorCode' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-19' -name: Get All AWS Activity From IP Address -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_all_backup_data_for_host.yml b/investigations/get_all_backup_data_for_host.yml deleted file mode 100644 index 0613cba076..0000000000 --- a/investigations/get_all_backup_data_for_host.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: '2017-06-19' -data_metadata: - data_source: - - Backup Systems - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup -description: Retrieve the backup logs for the last 2 weeks for a specific host in - order to investigate why backups are not completing successfully. -entities: - - dest -how_to_implement: The successfully implement this search you must first send your - backup logs to Splunk. -id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '1209600' - latest_time: '0' - search: '| search sourcetype="netbackup_logs" dest={dest}' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-12' -name: All backup logs for host -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_authentication_logs_for_endpoint.yml b/investigations/get_authentication_logs_for_endpoint.yml deleted file mode 100644 index 1cf090beb4..0000000000 --- a/investigations/get_authentication_logs_for_endpoint.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2017-04-10' -data_metadata: - data_models: - - Authentication - data_source: - - Authentication - providing_technologies: - - Microsoft Windows - - Linux - - macOS -description: This search returns all users that have attempted to access a particular - endpoint. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting authentication - logs from your various systems and populating the Authentication data model. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '43200' - latest_time: '1' - search: '| tstats count from datamodel=Authentication where Authentication.dest={dest} - by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action - | `drop_dm_object_name("Authentication")`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-11-01' -name: Get Authentication Logs For Endpoint -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_backup_logs_for_endpoint.yml b/investigations/get_backup_logs_for_endpoint.yml deleted file mode 100644 index b383a610d2..0000000000 --- a/investigations/get_backup_logs_for_endpoint.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: '2017-08-24' -data_metadata: - data_source: - - Backup Systems - data_sourcetypes: - - netbackup_logs - providing_technologies: - - Netbackup -description: This search will tell you the backup status from your netbackup_logs - of a specific endpoint for the last week. -entities: - - dest -how_to_implement: You must be ingesting your backup logs. -id: fdcfb369-1725-4c24-824a-22972d7f0d44 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '604800' - latest_time: '0' - search: '| search sourcetype="netbackup_logs" COMPUTERNAME={dest} | rename COMPUTERNAME - as dest, MESSAGE as signature | table _time, dest, signature' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-14' -name: Get Backup Logs For Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_certificate_for_domain.yml b/investigations/get_certificate_for_domain.yml deleted file mode 100644 index eeeae6a09b..0000000000 --- a/investigations/get_certificate_for_domain.yml +++ /dev/null @@ -1,45 +0,0 @@ -channel: ESCU -creation_date: '2019-04-29' -data_metadata: - data_models: - - Certificates - data_source: - - Certificate Logs - providing_technologies: - - Splunk Stream - - Bro -description: This search queries the Certificates datamodel and give you all the information - for a specific domain. Please note that the certificates issued by "Let's Encrypt" - are widely used by attackers. -entities: - - domain -how_to_implement: You must be ingesting your certificates or SSL logs from your network - traffic into your Certificates datamodel. Please note the wildcard(*) before domain - in the search syntax, we use to match for all domain and subdomain combinations -id: bc91a8cf-35e7-4bb2-2240-e756cc06fd73 -investigate: - splunk: - fields_required: - - domain - schedule: - earliest_time: "36000" - latest_time: "0" - search: '| tstats `summariesonly` count min(_time) as firstTime max(_time) as - lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*{domain} by - All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name - All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | - `drop_dm_object_name(All_Certificates)` | `drop_dm_object_name(SSL)` | rename - ssl_subject_common_name as domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-04-29' -name: Get Certificate logs for a domain -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_ec2_details_by_instance_id.yml b/investigations/get_ec2_details_by_instance_id.yml deleted file mode 100644 index 1c3982d28f..0000000000 --- a/investigations/get_ec2_details_by_instance_id.yml +++ /dev/null @@ -1,45 +0,0 @@ -creation_date: '2018-02-12' -data_metadata: - data_source: - - AWS Description - data_sourcetypes: - - aws:description - providing_technologies: - - AWS -description: This search queries AWS description logs and returns all the information - about a specific instance via the instanceId field -entities: - - instanceId -how_to_implement: In order to implement this search, you must install the AWS App - for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) - and configure your AWS description inputs. -id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 -investigate: - splunk: - fields_required: - - instanceId - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '0' - search: '| search sourcetype="aws:description" source="*:ec2_instances"| dedup - id sortby -_time | search id={instanceId} | spath output=tags path=tags | eval - tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) - | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, - launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account - ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as - "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", - state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-02-12' -name: Get EC2 Instance Details by instanceId -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_ec2_launch_details.yml b/investigations/get_ec2_launch_details.yml deleted file mode 100644 index 6a17644298..0000000000 --- a/investigations/get_ec2_launch_details.yml +++ /dev/null @@ -1,42 +0,0 @@ -creation_date: '2018-03-12' -data_metadata: - data_source: - - AWS CloudTrail Logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search returns some of the launch details for a EC2 instance. -entities: - - dest -how_to_implement: In order to implement this search, you must install the AWS App - for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) - and configure your AWS description inputs. -id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '0' - search: '| search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId={dest} - |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId - as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, - responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture - as architecture, responseElements.instancesSet.items{}.keyName as keyName | - table arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-03-12' -name: Get EC2 Launch Details -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_email_info.yml b/investigations/get_email_info.yml deleted file mode 100644 index a89117402a..0000000000 --- a/investigations/get_email_info.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: '2017-04-21' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: This search returns all the information Splunk might have collected a - specific email message over the last 2 hours. -entities: - - message_id -how_to_implement: To successfully implement this search you must be ingesting your - email logs or capturing unencrypted network traffic which contains email communications. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd75 -investigate: - splunk: - fields_required: - - message_id - schedule: - cron_schedule: '' - earliest_time: '0' - latest_time: '7200' - search: '| from datamodel Email.All_Email | search message_id={message_id}' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-11-09' -name: Get Email Info -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_emails_from_sender.yml b/investigations/get_emails_from_sender.yml deleted file mode 100644 index 4d7e9a875f..0000000000 --- a/investigations/get_emails_from_sender.yml +++ /dev/null @@ -1,38 +0,0 @@ -creation_date: '2017-04-21' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: This search returns all the emails from a specific sender over the last - 24 and next hours. -entities: - - src_user -how_to_implement: To successfully implement this search you must ingest your email - logs or capture unencrypted email communications within network traffic, and populate - the Email data model. -id: 5df39b3f-447d-4869-b673-8f45ad4616fe -investigate: - splunk: - fields_required: - - src_user - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '86400' - search: '| from datamodel Email.All_Email | search src_user={src_user}' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-11-09' -name: Get Emails From Specific Sender -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_emails_from_source.yml b/investigations/get_emails_from_source.yml deleted file mode 100644 index 8b87108b36..0000000000 --- a/investigations/get_emails_from_source.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2019-02-21' -data_metadata: - data_models: - - Email - data_source: - - Email - providing_technologies: - - Microsoft Exchange -description: This search returns a list of all email sources seen in the 48 hours - prior to the notable event to 24 hours after, and the number of emails from each - source. -entities: - - src -how_to_implement: To successfully implement this search you must ingest your email - logs or capture unencrypted email communications within network traffic, and populate - the Email data model. -id: ddc7af28-c34d-4392-af93-7f29a4e8806c -investigate: - splunk: - fields_required: - - src - schedule: - cron_schedule: '' - earliest_time: '172800' - latest_time: '86400' - search: '|tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) - as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email - by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2019-02-21' -name: Get History Of Email Sources -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_logon_rights_modifications_for_endpoint.yml b/investigations/get_logon_rights_modifications_for_endpoint.yml deleted file mode 100644 index 8dc928e47e..0000000000 --- a/investigations/get_logon_rights_modifications_for_endpoint.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2017-08-16' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search allows you to retrieve any modifications to logon rights - associated with a specific host. -entities: - - dest -how_to_implement: To successfully implement this search you must be ingesting your - Windows event logs -id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '86400' - search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) - dest={dest} | rename user as "Account Modified" | table _time, dest, "Account - Modified", Access_Right, signature' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-12' -name: Get Logon Rights Modifications For Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_logon_rights_modifications_for_user.yml b/investigations/get_logon_rights_modifications_for_user.yml deleted file mode 100644 index f7b482e888..0000000000 --- a/investigations/get_logon_rights_modifications_for_user.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2017-08-16' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search allows you to retrieve any modifications to logon rights - for a specific user account. -entities: - - user -how_to_implement: To successfully implement this search you must be ingesting your - Windows event logs -id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb -investigate: - splunk: - fields_required: - - user - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '86400' - search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) - user={user} | rename user as "Account Modified" | table _time, dest, "Account - Modified", Access_Right, signature' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-02-27' -name: Get Logon Rights Modifications For User -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_network_traffic_from_src_ip.yml b/investigations/get_network_traffic_from_src_ip.yml deleted file mode 100644 index 0f537671bf..0000000000 --- a/investigations/get_network_traffic_from_src_ip.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2018-06-15' -data_metadata: - data_models: - - Network_Traffic - data_source: - - Network Communications - providing_technologies: - - Splunk Stream - - Bro - - Palo Alto Firewall -description: This search allows you to find all the network traffic from a specific - IP address. -entities: - - src_ip -how_to_implement: To successfully implement this search, you must be ingesting your - web-traffic logs and populating the web data model. -id: 9df9ca9c-a02b-4f48-9eba-0bac55179050 -investigate: - splunk: - fields_required: - - src_ip - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| from datamodel Network_Traffic.All_Traffic | search src_ip={src_ip}' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-15' -name: Investigate Network Traffic From src_ip -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_notable_history_for_endpoint.yml b/investigations/get_notable_history_for_endpoint.yml deleted file mode 100644 index c0766f5023..0000000000 --- a/investigations/get_notable_history_for_endpoint.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: '2017-03-15' -data_metadata: - data_source: - - Notable Events - providing_technologies: - - Splunk Enterprise Security -description: This search queries the notable index and returns all the Notable Events - for the particular destination host, giving the analyst an overview of the incidents - that may have occurred with the host under investigation. -entities: - - dest -how_to_implement: If you are using Enterprise Security you are likely already creating - notable events with your correlation rules. No additional configuration is necessary. -id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '864000' - latest_time: '86400' - search: '| search `notable` | search dest={dest} | table _time, rule_name, owner, - priority, severity, status_description' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-20' -name: Get Notable History -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_notable_info.yml b/investigations/get_notable_info.yml deleted file mode 100644 index 229580c3bb..0000000000 --- a/investigations/get_notable_info.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: '2017-03-15' -data_metadata: - data_source: - - Notable Events - providing_technologies: - - Splunk Enterprise Security -description: This search queries the notable index to retrieve detailed information - captured within the notable. Every notable has a unique ID associated with it, which - is used to point us directly to the notable event under investigation. -entities: - - event_id -how_to_implement: If you are using Enterprise Security you are likely already creating - notable events with your correlation rules. No additional configuration is necessary. -id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 -investigate: - splunk: - fields_required: - - event_id - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| search `notable_by_id({event_id})` | table time, rule_name, dest, dest_asset_id, - dest_owner, priority, severity, owner, status_description' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-20' -name: Get Notable Info -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_okta_activity_by_app.yml b/investigations/get_okta_activity_by_app.yml deleted file mode 100644 index 3cc1ea16e9..0000000000 --- a/investigations/get_okta_activity_by_app.yml +++ /dev/null @@ -1,34 +0,0 @@ -creation_date: '2020-04-02' -data_metadata: - eventtype: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: This search returns all okta events associated with a specific app -entities: - - app -how_to_implement: You must be ingesting Okta logs -id: 420eb1b8-2992-45d1-80cf-0b1b2759524d -investigate: - splunk: - fields_required: - - app - schedule: - earliest_time: "86400" - latest_time: "14400" - search: 'eventtype=okta_log app={app} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-04-02' -name: Investigate Okta Activity by app -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_okta_activity_by_ip.yml b/investigations/get_okta_activity_by_ip.yml deleted file mode 100644 index d99a0baa32..0000000000 --- a/investigations/get_okta_activity_by_ip.yml +++ /dev/null @@ -1,34 +0,0 @@ -creation_date: '2020-04-02' -data_metadata: - eventtype: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: This search returns all okta events from a specific IP address. -entities: - - src_ip -how_to_implement: You must be ingesting Okta logs -id: 56aae066-d619-477c-93e3-3fb83b2d23c3 -investigate: - splunk: - fields_required: - - user - schedule: - earliest_time: "86400" - latest_time: "14400" - search: 'eventtype=okta_log src_ip={src_ip} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-04-02' -name: Investigate Okta Activity by IP Address -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_okta_user_activity.yml b/investigations/get_okta_user_activity.yml deleted file mode 100644 index 05ec99314d..0000000000 --- a/investigations/get_okta_user_activity.yml +++ /dev/null @@ -1,34 +0,0 @@ -creation_date: '2020-04-02' -data_metadata: - eventtype: - - okta_log - data_source: - - Okta - providing_technologies: - - Okta -description: This search returns all okta events by a specific user -entities: - - user -how_to_implement: You must be ingesting Okta logs -id: 24ff145d-4d16-420a-b047-480f2a51c403 -investigate: - splunk: - fields_required: - - user - schedule: - earliest_time: "86400" - latest_time: "14400" - search: 'eventtype=okta_log user={user} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason' -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-04-02' -name: Investigate User Activities In Okta -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_parent_process_info.yml b/investigations/get_parent_process_info.yml deleted file mode 100644 index e8f110bb04..0000000000 --- a/investigations/get_parent_process_info.yml +++ /dev/null @@ -1,49 +0,0 @@ -creation_date: '2017-08-22' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search queries the Endpoint data model to give you details about - the parent process of a process running on a host which is under investigation. - Enter the values of the process name in question and the dest -entities: - - process_name - - dest -how_to_implement: You must be ingesting endpoint data that tracks process activity, - including parent-child relationships from your endpoints to populate the Endpoint - data model in the Processes node. The command-line arguments are mapped to the "process" - field in the Endpoint data model. -id: fecf2918-670d-4f1c-872b-3d7317a41bf9 -investigate: - splunk: - fields_required: - - process_name - - dest - schedule: - earliest_time: "86400" - latest_time: "0" - search: '| tstats `summariesonly` count values(Processes.process) as process min(_time) - as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where - Processes.process_name = {process_name} Processes.dest = {dest} by Processes.user - Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` - | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-02-28' -name: Get Parent Process Info -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_process_creating_dns_traffic.yml b/investigations/get_process_creating_dns_traffic.yml deleted file mode 100644 index 1b8c400601..0000000000 --- a/investigations/get_process_creating_dns_traffic.yml +++ /dev/null @@ -1,50 +0,0 @@ -creation_date: '2017-04-10' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: While investigating, an analyst will want to know what process and parent_process - is responsible for generating suspicious DNS traffic. Use the following search and - enter the value of `dest` in the search to get specific details on the process responsible - for creating the DNS traffic. -entities: - - dest -how_to_implement: You must be ingesting endpoint data that associates processes with - network events into the Endpoint datamodel. This can come from endpoint protection - products such as carbon black, or endpoint data sources such as Sysmon. -id: 910e6512-edc9-4f93-ba24-5b786f47a672 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '86400' - search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from - datamodel=Endpoint.Processes where Processes.dest = {dest} by Processes.parent_process - Processes.process_name Processes.user Processes.dest Processes.process_id | - `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search - [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 - by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as - dest]' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-04-01' -name: Get Process Responsible For The DNS Traffic -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_process_file_activity.yml b/investigations/get_process_file_activity.yml deleted file mode 100644 index c525a3e26c..0000000000 --- a/investigations/get_process_file_activity.yml +++ /dev/null @@ -1,47 +0,0 @@ -creation_date: '2019-11-06' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search returns the file activity for a specific process on a specific endpoint -entities: - - process_id - - dest -how_to_implement: To successfully implement this search you must be ingesting endpoint - data and populating the Endpoint data model. -id: 6a9ad4d9-6ef2-4b85-953f-a37ab256acd5 -investigate: - splunk: - fields_required: - - process_id - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '7200' - search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, - values(Filesystem.process_id) as process_id from - datamodel=Endpoint.Filesystem where Filesystem.dest={dest} Filesystem.process_id={process_id} - by Filesystem.file_path, Filesystem.action, _time - | `drop_dm_object_name(Filesystem)` | sort _time - | table _time, process_id, dest, action, file_name, file_path' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-11-06' -name: Get Process File Activity -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_process_info.yml b/investigations/get_process_info.yml deleted file mode 100644 index b1fdd1e0fd..0000000000 --- a/investigations/get_process_info.yml +++ /dev/null @@ -1,47 +0,0 @@ -creation_date: '2017-03-15' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search queries the Endpoint data model to give you details about - the process running on a host which is under investigation. To gather the process - info, enter the values for the process name in question and the destination IP address. -entities: - - process_name - - dest -how_to_implement: To successfully implement this search you must be ingesting endpoint - data and populating the Endpoint data model. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 -investigate: - splunk: - fields_required: - - process_name - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '7200' - search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from - datamodel=Endpoint.Processes where Proceses.dest={dest} Proceses.process_name={process_name} - by Processes.parent_process Processes.process_name Processes.user Processes.dest - | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` ' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-04-01' -name: Get Process Info -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_process_info_for_port.yml b/investigations/get_process_info_for_port.yml deleted file mode 100644 index 2fc5085186..0000000000 --- a/investigations/get_process_info_for_port.yml +++ /dev/null @@ -1,47 +0,0 @@ -creation_date: '2017-06-25' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: This search will return information about the process associated with - observed network traffic to a specific destination port from a specific host. -entities: - - dest_port - - dest -how_to_implement: To successfully implement this search you must be ingesting endpoint - data that associates processes with network events and populate the Endpoint Datamodel -id: 9925d08f-561e-4faa-8912-e3888a842341 -investigate: - splunk: - fields_required: - - dest_port - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '7200' - search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from - datamodel=Endpoint.Processes where Processes.dest = {dest} by Processes.process_name - Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` - | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count - from datamodel=Endpoint.Ports where Ports.dest_port={dest_port} by Ports.process_id - Ports.src | `drop_dm_object_name(Ports)` | rename src as dest]' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-04-01' -name: Get Process Information For Port Activity -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_process_registry_activity.yml b/investigations/get_process_registry_activity.yml deleted file mode 100644 index ebe7a47ca8..0000000000 --- a/investigations/get_process_registry_activity.yml +++ /dev/null @@ -1,47 +0,0 @@ -creation_date: '2019-11-06' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint Intel - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search returns the registry activity for a specific process on a specific endpoint -entities: - - process_id - - dest -how_to_implement: To successfully implement this search you must be ingesting endpoint - data and populating the Endpoint data model. -id: d8362a34-b78a-4364-9733-59b505f5b8d5 -investigate: - splunk: - fields_required: - - process_id - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '7200' - search: '| tstats `security_content_summariesonly` values(Registry.registry_key_name) as registry_key_name, - values(Registry.dest) as dest, values(Registry.process_id) as process_id from - datamodel=Endpoint.Registry where Registry.process_id={process_id} AND Registry.dest={dest} - by Registry.registry_path, Registry.action, _time - | `drop_dm_object_name(Registry)` | sort _time - | table _time, process_id, dest, action, registry_key_name, registry_path' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-11-06' -name: Get Process Registry Activity -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_rdp_authentications.yml b/investigations/get_rdp_authentications.yml deleted file mode 100644 index 70cb55493a..0000000000 --- a/investigations/get_rdp_authentications.yml +++ /dev/null @@ -1,43 +0,0 @@ -creation_date: '2018-12-14' -data_metadata: - data_models: - - Authentication - data_source: - - Authentication - providing_technologies: - - Microsoft Windows -description: 'This search returns the source, destination, and user for all successful - remote-desktop authentications. A successful authentication after a brute-force - attack on a destination machine is suspicious behavior. ' -entities: - - dest -how_to_implement: You must be populating the Authentication data model with security - events from your Windows event logs. -id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '86400' - latest_time: '0' - search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as - lastTime from datamodel=Authentication where Authentication.signature_id=4624 - Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app - Authentication.user Authentication.signature Authentication.src_nt_domain | - `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")`| - table firstTime lastTime src src_nt_domain dest user app count | sort count' -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2018-12-14' -name: Investigate Successful Remote Desktop Authentications -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_registry_activities.yml b/investigations/get_registry_activities.yml deleted file mode 100644 index e15083c7e1..0000000000 --- a/investigations/get_registry_activities.yml +++ /dev/null @@ -1,49 +0,0 @@ -creation_date: '2018-08-07' -data_metadata: - data_models: - - Endpoint - data_source: - - Endpoint - providing_technologies: - - Carbon Black Response - - CrowdStrike Falcon - - Sysmon - - Tanium - - Ziften -description: This search queries the Endpoint Datamodel to give you details of the - latest registry values for a specific destination computer. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting information - on registry changes that include the name of the process responsible for the changes - from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` - nodes. -id: fecf2918-670d-4f1c-872b-3d7317a41xf9 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '0' - latest_time: '86400' - search: '| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path - values(Registry.registry_key_name) as registry_key_name count FROM datamodel=Endpoint.Registry - where Registry.dest = "{dest}" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` - | join [| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.process_name) - as process_name values(Processes.parent_process_name) as parent_process_name - FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by - Processes.process_id | `drop_dm_object_name("Processes")`]' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-03-01' -name: Get Registry Activities -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/get_risk_for_endpoint.yml b/investigations/get_risk_for_endpoint.yml deleted file mode 100644 index 37292208d3..0000000000 --- a/investigations/get_risk_for_endpoint.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2017-10-14' -data_metadata: - data_models: - - Risk - data_source: - - Risk Scores - providing_technologies: - - Splunk Enterprise Security -description: 'For the last 7 days, the search will query the Risk data model in Splunk - Enterprise Security and calculate the count, sum of the risk\_scores, names of the - correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) ' -entities: - - dest -how_to_implement: Enable the correlation searches included in Splunk Enterprise Security - that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -id: fdcfb369-1725-4c24-824a-22972d7f0d65 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '604800' - latest_time: '0' - search: '| from datamodel:Risk.All_Risk | search risk_object_type=system risk_object={dest} - | stats count sum(risk_score) as risk_score values(search_name) min(_time) - as firstTime max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-10-19' -name: Get Risk Modifiers For Endpoint -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_risk_for_user.yml b/investigations/get_risk_for_user.yml deleted file mode 100644 index 5ebb23dce1..0000000000 --- a/investigations/get_risk_for_user.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2017-10-14' -data_metadata: - data_models: - - Risk - data_source: - - Risk Scores - providing_technologies: - - Splunk Enterprise Security -description: 'For the last 7 days, the search will query the Risk data model in Splunk - Enterprise Security and calculate the count, sum of the risk_scores, names of the - correlation searches that contributed to create a risk score for a specific user ' -entities: - - user -how_to_implement: Enable the correlation searches included in Splunk Enterprise Security - that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -id: fdcfb369-1725-4c24-824a-22972d7f0d55 -investigate: - splunk: - fields_required: - - user - schedule: - cron_schedule: '' - earliest_time: '604800' - latest_time: '0' - search: '| from datamodel:Risk.All_Risk | search risk_object_type=user risk_object={user} - | stats count sum(risk_score) as risk_score values(search_name) min(_time) - as firstTime max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` ' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-10-19' -name: Get Risk Modifiers For User -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_update_logs_for_endpoint.yml b/investigations/get_update_logs_for_endpoint.yml deleted file mode 100644 index 8408ba0307..0000000000 --- a/investigations/get_update_logs_for_endpoint.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2017-08-24' -data_metadata: - data_models: - - Updates - data_source: - - Update Logs - providing_technologies: - - Microsoft Windows - - Linux - - macOS -description: This search will tell you give you the update logs for a specific endpoint - for the last week. -entities: - - dest -how_to_implement: You need to be ingesting the update logs from your various systems. -id: d98675ed-da43-4a7e-96a7-eeca3232ba8e -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '604800' - latest_time: '0' - search: '| from datamodel Updates.Updates | search (vendor_product="Microsoft - Windows" OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest={dest}' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-08-24' -name: Get Update Logs For Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_user_info_from_identity_table.yml b/investigations/get_user_info_from_identity_table.yml deleted file mode 100644 index 1fe5c05b27..0000000000 --- a/investigations/get_user_info_from_identity_table.yml +++ /dev/null @@ -1,35 +0,0 @@ -creation_date: '2017-04-10' -data_metadata: - data_source: - - Identity Table - providing_technologies: - - Splunk Enterprise Security -description: Gather more information about the user identified in the Notable Event. -entities: - - user -how_to_implement: To successfully implement this search you must have populated the - identity table with information about your users. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 -investigate: - splunk: - fields_required: - - user - schedule: - cron_schedule: '' - earliest_time: '864000' - latest_time: '86400' - search: '| `identities` | search identity={user} | table _time, identity, first, - last, email, category, watchlist' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-20' -name: Get User Information from Identity Table -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_vulnerability_logs_for_endpoint.yml b/investigations/get_vulnerability_logs_for_endpoint.yml deleted file mode 100644 index b5d136ab85..0000000000 --- a/investigations/get_vulnerability_logs_for_endpoint.yml +++ /dev/null @@ -1,36 +0,0 @@ -creation_date: '2017-08-24' -data_metadata: - data_models: - - Vulnerabilities - data_source: - - Vulnerability Scanner - providing_technologies: - - Nessus -description: This search will show you any vulnerabilities noted for a specific endpoint - for the last week. -entities: - - dest -how_to_implement: You need to be ingesting the logs from your vulnerability scanner. -id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '604800' - latest_time: '0' - search: '| from datamodel Vulnerabilities.Vulnerabilities | search dest={dest}' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-10' -name: Get Vulnerability Logs For Endpoint -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_web_activity.yml b/investigations/get_web_activity.yml deleted file mode 100644 index d35f92610e..0000000000 --- a/investigations/get_web_activity.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2017-04-21' -data_metadata: - data_models: - - Web - data_source: - - Web - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: This search allows you to find all the web activity from a specific host. - During an investigation, it is important to profile web activity to characterize - user or host activity. -entities: - - dest -how_to_implement: To successfully implement this search you must be ingesting your - web traffic and populating the Web data model. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| from datamodel Web.Web | search src={dest}' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-11-09' -name: Investigate Web Activity From Host -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_web_activity_by_src_ip.yml b/investigations/get_web_activity_by_src_ip.yml deleted file mode 100644 index 9aa2fbb583..0000000000 --- a/investigations/get_web_activity_by_src_ip.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2018-06-15' -data_metadata: - data_models: - - Web - data_source: - - Web - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: This search searches for all web activity from a specific host. During - an investigation, it is important to profile web activity to characterize user or - host activity. -entities: - - src_ip -how_to_implement: To successfully implement this search, you must be ingesting your - web traffic and populating the web data model. -id: 2f5b960b-71df-49c0-affc-74992ce60e45 -investigate: - splunk: - fields_required: - - src_ip - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| from datamodel Web.Web | search src={src_ip}' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-15' -name: Investigate Web Activity From src_ip -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_web_posts_by_src.yml b/investigations/get_web_posts_by_src.yml deleted file mode 100644 index e562201b4f..0000000000 --- a/investigations/get_web_posts_by_src.yml +++ /dev/null @@ -1,43 +0,0 @@ -creation_date: '2018-12-06' -data_metadata: - data_models: - - Web - data_source: - - Web - providing_technologies: - - Splunk Stream - - Bro - - Bluecoat - - Palo Alto Firewall -description: 'This investigative search retrieves POST requests from a specified source - IP or hostname. Identifying the POST requests, as well as their associated destination - URLs and user agent(s), may help you scope and characterize the suspicious traffic. ' -entities: - - src -how_to_implement: To successfully implement this search, you must be ingesting your - web-traffic logs and populating the web data model. -id: f5c39fac-205c-4e07-9004-8fd61ea3431a -investigate: - splunk: - fields_required: - - src - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by - Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| - where like(src, "{src}") and like(http_method, "POST")' -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2018-12-06' -name: Investigate Web POSTs From src -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_web_session_info.yml b/investigations/get_web_session_info.yml deleted file mode 100644 index 06d1178bd9..0000000000 --- a/investigations/get_web_session_info.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2018-10-08' -data_metadata: - data_source: - - Network Communications - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search helps an analyst investigate a notable event to find out - more about a specific web session. The search looks for a specific web session ID - in the HTTP web traffic and outputs the URL and user agents, grouped by source IP - address and HTTP status code. -entities: - - session_id -how_to_implement: This search leverages data extracted from Stream:HTTP. You must - configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment - server. -id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89 -investigate: - splunk: - fields_required: - - session_id - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| search sourcetype=stream:http {session_id} | stats values(url) values(http_user_agent) - by src_ip status' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-10-08' -name: Get Web Session Information via session_id -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/get_wmi_sysmon_events.yml b/investigations/get_wmi_sysmon_events.yml deleted file mode 100644 index 08437b88ed..0000000000 --- a/investigations/get_wmi_sysmon_events.yml +++ /dev/null @@ -1,42 +0,0 @@ -creation_date: '2018-10-23' -data_metadata: - data_source: - - Endpoint Intel - data_sourcetypes: - - XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - providing_technologies: - - Sysmon -description: This search queries Sysmon WMI events for the host of interest. -entities: - - process - - dest -how_to_implement: To successfully implement this search, you must be collecting Sysmon - data using Sysmon version 6.1 or greater and have Sysmon configured to generate - events for WMI activity. In addition, you must have at least version 6.0.4 of the - Sysmon TA installed to properly parse the fields. -id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 -investigate: - splunk: - fields_required: - - process - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '7200' - search: sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 - EventCode<22 host={dest} | rename host as dest | table _time, dest, user, Name, - Operation, EventType, Type, Query, Consumer, Filter -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2018-10-23' -name: Get Sysmon WMI Activity for Host -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_cloud_compute_instance_activities.yml b/investigations/investigate_cloud_compute_instance_activities.yml deleted file mode 100644 index e53eed6247..0000000000 --- a/investigations/investigate_cloud_compute_instance_activities.yml +++ /dev/null @@ -1,38 +0,0 @@ -creation_date: '2019-10-02' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search returns a logs of events that operated on the compute instance. -entities: - - dest -how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs - and have the Security Research cloud data model installed. -id: 33a95cf2-900c-4636-8fca-5c5f71474720 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '7200' - latest_time: '0' - search: '| from datamodel:Cloud_Infrastructure.Compute | search dest={dest} | fields - _*' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-12' -name: Investigate Cloud Compute Instance Activities -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_failed_logins_for_multiple_destinations.yml b/investigations/investigate_failed_logins_for_multiple_destinations.yml deleted file mode 100644 index 18c1f8b129..0000000000 --- a/investigations/investigate_failed_logins_for_multiple_destinations.yml +++ /dev/null @@ -1,41 +0,0 @@ -creation_date: '2019-12-10' -data_metadata: - data_models: - - Authentication - data_source: - - Authentication - providing_technologies: - - Microsoft Windows - - Linux - - macOS -description: This search returns failed logins to multiple destinations by user. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting authentication - logs from your various systems and populating the Authentication data model. -id: 097e8030-8662-4254-a735-bf0bdda696e3 -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '-7d' - latest_time: 'now' - search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user - | where distinct_count_dest > 1 - | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` - | `drop_dm_object_name("Authentication")`' -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -modification_date: '2019-12-10' -name: Investigate Failed Logins for Multiple Destinations -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_pass_the_hash_attempts.yml b/investigations/investigate_pass_the_hash_attempts.yml deleted file mode 100644 index 747ac49248..0000000000 --- a/investigations/investigate_pass_the_hash_attempts.yml +++ /dev/null @@ -1,39 +0,0 @@ -creation_date: '2019-12-10' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search hunts for dumped NTLM hashes used for pass the hash. -entities: - - dest -how_to_implement: To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -id: ed3fff45-cba6-4990-983f-6fac72bee659 -investigate: - splunk: - fields_required: - - dest - macros: - - wineventlog_security - schedule: - cron_schedule: '' - earliest_time: '-24h' - latest_time: 'now' - search: '`wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate - | stats count earliest(_time) as first_login latest(_time) as last_login by src_user dest - | `security_content_ctime(first_login)` | `security_content_ctime(last_login)`' -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -modification_date: '2019-12-10' -name: Investigate Pass the Hash Attempts -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_pass_the_ticket_attempts.yml b/investigations/investigate_pass_the_ticket_attempts.yml deleted file mode 100644 index 5e7c124aad..0000000000 --- a/investigations/investigate_pass_the_ticket_attempts.yml +++ /dev/null @@ -1,43 +0,0 @@ -creation_date: '2019-12-10' -data_metadata: - data_eventtypes: - - wineventlog_security - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows -description: This search hunts for dumped kerberos ticket from LSASS memory. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. - -id: 990007ad-d798-4b29-ab2f-f0034144c937 -investigate: - splunk: - fields_required: - - dest - macros: - - wineventlog_security - schedule: - cron_schedule: '' - earliest_time: '-24h' - latest_time: 'now' - search: '`wineventlog_security` EventCode=4768 OR EventCode=4769 - | rex field=user "(?[^\@]+)" - | stats count BY new_user, dest, EventCode - | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest - | where sum_count/max_count!=2 - | rename new_user AS user' -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -modification_date: '2019-12-10' -name: Investigate Pass the Ticket Attempts -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_previous_unseen_user.yml b/investigations/investigate_previous_unseen_user.yml deleted file mode 100644 index 5c02e1e00f..0000000000 --- a/investigations/investigate_previous_unseen_user.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2019-12-10' -data_metadata: - data_models: - - Authentication - data_source: - - Authentication - providing_technologies: - - Microsoft Windows - - Linux - - macOS -description: This search returns previous unseen user, which didn't log in for 30 days. -entities: - - dest -how_to_implement: To successfully implement this search you need to be ingesting authentication - logs from your various systems and populating the Authentication data model. -id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad -investigate: - splunk: - fields_required: - - dest - schedule: - cron_schedule: '' - earliest_time: '-60d' - latest_time: 'now' - search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user - | bucket _time span=30d - | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user - | where count=1 - | where first_login >= relative_time(now(), "-30d") - | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` - | `drop_dm_object_name("Authentication")`' -maintainers: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -modification_date: '2019-12-10' -name: Investigate Previous Unseen User -original_authors: - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/investigate_user_activities_in_all_cloud_regions.yml b/investigations/investigate_user_activities_in_all_cloud_regions.yml deleted file mode 100644 index 1e1a22fab5..0000000000 --- a/investigations/investigate_user_activities_in_all_cloud_regions.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2019-10-02' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search lists all the logged cloud infrastructure activities by a - specific cloud user -entities: - - region - - src_user -how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs - and have the Security Research cloud data model installed. -id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a -investigate: - splunk: - fields_required: - - region - - src_user - schedule: - earliest_time: "86400" - latest_time: "14400" - search: '| from datamodel:Cloud_Infrastructure.Compute | search user={src_user} | fields - _*' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-04-30' -name: Investigate User Activities In All Cloud Regions -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/investigate_user_activities_in_single_cloud_region.yml b/investigations/investigate_user_activities_in_single_cloud_region.yml deleted file mode 100644 index 2bac31dd9b..0000000000 --- a/investigations/investigate_user_activities_in_single_cloud_region.yml +++ /dev/null @@ -1,40 +0,0 @@ -creation_date: '2019-10-02' -data_metadata: - data_models: - - Cloud_Infrastructure - data_source: - - Cloud Infrastructure Logs - providing_technologies: - - AWS - - Azure - - GCP -description: This search lists all the logged cloud infrastructure activities by a - specific cloud user in a specific cloud region -entities: - - region - - src_user -how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs - and have the Security Research cloud data model installed. -id: 3dc3a8e7-394b-44ae-8262-4ef8e90b723d -investigate: - splunk: - fields_required: - - region - - src_user - schedule: - earliest_time: "86400" - latest_time: "14400" - search: '| from datamodel:Cloud_Infrastructure.Compute | search region={region} user={src_user} | fields - _*' -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-04-30' -name: Investigate User Activities In Single Cloud Region -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -spec_version: 2 -type: splunk -version: '2.0' diff --git a/investigations/kubernetes_aws_activity_by_src_ip.yml b/investigations/kubernetes_aws_activity_by_src_ip.yml deleted file mode 100644 index 832a98c410..0000000000 --- a/investigations/kubernetes_aws_activity_by_src_ip.yml +++ /dev/null @@ -1,37 +0,0 @@ -creation_date: "2020-03-24" -data_metadata: - data_source: - - AWS CloudWatch EKS Logs - data_sourcetypes: - - aws:cloudwatchlogs:eks - providing_technologies: - - AWS -description: This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address -entities: - - src_ip -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch EKS inputs. -id: a636cca4-7434-4a15-a278-c70734938e39 -investigate: - splunk: - fields_required: - - src_ip - schedule: - cron_schedule: 0 * * * * - earliest_time: -70m@m - latest_time: -10m@m - search: sourcetype="aws:cloudwatchlogs:eks" sourceIPs{}={src_ip}|rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -modification_date: "2020-04-13" -name: Amazon EKS Kubernetes activity by src_ip -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -references: [] -security_domain: threat -spec_version: 2 -type: splunk -version: "1.0" diff --git a/investigations/kubernetes_gcp_activity_by_src_ip.yml b/investigations/kubernetes_gcp_activity_by_src_ip.yml deleted file mode 100644 index 89c384a208..0000000000 --- a/investigations/kubernetes_gcp_activity_by_src_ip.yml +++ /dev/null @@ -1,35 +0,0 @@ -creation_date: "2020-03-24" -data_metadata: - data_source: - - "GCP Stackdriver logs" - data_sourcetypes: - - "google:gcp:pubsub:message" - providing_technologies: - - GCP -description: This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address -entities: - - src_user -how_to_implement: "You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs." -id: c00e7626-92cc-4e06-9a51-b6db0a50bd1f -investigate: - splunk: - fields_required: - - src_user - schedule: - cron_schedule: "0 * * * *" - earliest_time: "-70m@m" - latest_time: "-10m@m" - search: sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp={src_ip} | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name data.resource.type -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -modification_date: "2020-04-13" -name: GCP Kubernetes activity by src_ip -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: "Rod Soto" -spec_version: 2 -type: splunk -version: "1.0" diff --git a/investigations/suspicious_email_attachment_investigate_and_delete.yml b/investigations/suspicious_email_attachment_investigate_and_delete.yml deleted file mode 100644 index 646d691584..0000000000 --- a/investigations/suspicious_email_attachment_investigate_and_delete.yml +++ /dev/null @@ -1,49 +0,0 @@ -creation_date: '2019-06-03' -data_metadata: - data_source: - - Microsoft Exchange - - SMTP - - Cuckoo - - Splunk Enterprise Security - - VirusTotal - - DeepSight - providing_technologies: - - Microsoft Exchange - - SMTP - - Cuckoo - - Splunk Enterprise Security - - VirusTotal - - DeepSight -description: Investigate an email with a suspicious file attachment detected by Splunk - Enterprise Security. Detonate the file attachment in a sandbox, gather network behavior - from the sandbox results, and pivot on those network indicators with both external - reputation queries and internal Splunk Common Information Model searches. After - confirming the results with an analyst prompt, delete the email from the user's - inbox, hopefully before they have opened it. -how_to_implement: Synchronize the community playbook repository in Phantom, then open - the playbook and follow the deployment notes to configure it for your environment. -id: 3096f721-8842-42ce-2fc7-742d8372b712 -investigate: - phantom: - phantom_server: automation (hostname) - playbook_name: community/suspicious_email_attachment_investigate_and_delete - playbook_url: https://my.phantom.us/4.2/playbook/suspicious-email-attachment-investigate-and-delete/ - schedule: - cron_schedule: 0 * * * * - earliest_time: '0' - latest_time: '86400' - sensitivity: green - severity: medium -maintainers: - - company: Splunk - email: proyer@splunk.com - name: Philip Royer -modification_date: '2019-06-03' -name: Suspicious Email Attachment Investigate and Delete -original_authors: - - company: Splunk - email: proyer@splunk.com - name: Philip Royer -spec_version: 2 -type: phantom -version: '1.0' diff --git a/investigations/suspicious_strings_in_HTTP_header.yml b/investigations/suspicious_strings_in_HTTP_header.yml deleted file mode 100644 index ae696d9707..0000000000 --- a/investigations/suspicious_strings_in_HTTP_header.yml +++ /dev/null @@ -1,49 +0,0 @@ -creation_date: '2017-06-26' -data_metadata: - data_source: - - Network Communications - data_sourcetypes: - - stream:http - providing_technologies: - - Splunk Stream -description: This search helps an analyst investigate a notable event related to a - potential Apache Struts exploitation. To investigate, we will want to isolate and - analyze the "payload" or the commands that were passed to the vulnerable hosts by - creating a few regular expressions to carve out the commands focusing on common - keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns - these suspicious strings found in the HTTP logs of the system of interest. -entities: - - src_ip - - dest_ip -how_to_implement: This particular search leverages data extracted from Stream:HTTP. - You must configure the http stream using the Splunk Stream App on your Splunk Stream - deployment server to extract the cs_content_type field. -id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89 -investigate: - splunk: - fields_required: - - src_ip - - dest_ip - schedule: - cron_schedule: '' - earliest_time: '3600' - latest_time: '3600' - search: '| search sourcetype=stream:http src_ip="{src_ip}" dest_ip="{dest_ip}" - | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length - > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, - "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious - Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, - suspicious_strings, cs_content_type, cs_content_type_length, url' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-10-20' -name: Investigate Suspicious Strings in HTTP Header -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '1.0' diff --git a/investigations/user_activity_via_cloudtrail.yml b/investigations/user_activity_via_cloudtrail.yml deleted file mode 100644 index 895d3e6e14..0000000000 --- a/investigations/user_activity_via_cloudtrail.yml +++ /dev/null @@ -1,44 +0,0 @@ -creation_date: '2018-01-22' -data_metadata: - data_source: - - AWS CloudTrail logs - data_sourcetypes: - - aws:cloudtrail - providing_technologies: - - AWS -description: This search lists all the logged CloudTrail activities by a specific - user ARN and will create a table containing the source of the user, the region of - the activity, the name and type of the event, the action taken, and all the user's - identity information. -entities: - - user -how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) - and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail - inputs. -id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 -investigate: - splunk: - fields_required: - - user - schedule: - earliest_time: "72000" - latest_time: "36000" - search: '| search sourcetype=aws:cloudtrail userIdentity.arn={user} | table _time - userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src - awsRegion eventName eventType' -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2019-04-30' -name: AWS Investigate User Activities By ARN -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: splunk -version: '2.0' diff --git a/lookups/mitre_enrichment.csv b/lookups/mitre_enrichment.csv new file mode 100644 index 0000000000..2ee84a99fd --- /dev/null +++ b/lookups/mitre_enrichment.csv @@ -0,0 +1,267 @@ +mitre_id,technique,tactics,groups +T1531,Account Access Removal,Impact,no +T1506,Web Session Cookie,Defense Evasion|Lateral Movement,no +T1539,Steal Web Session Cookie,Credential Access,no +T1529,System Shutdown/Reboot,Impact,Lazarus Group|APT38|APT37 +T1519,Emond,Persistence|Privilege Escalation,no +T1518,Software Discovery,Discovery,no +T1534,Internal Spearphishing,Lateral Movement,no +T1528,Steal Application Access Token,Credential Access,APT28 +T1522,Cloud Instance Metadata API,Credential Access,no +T1536,Revert Cloud Instance,Defense Evasion,no +T1535,Unused/Unsupported Cloud Regions,Defense Evasion,no +T1525,Implant Container Image,Persistence,no +T1538,Cloud Service Dashboard,Discovery,no +T1530,Data from Cloud Storage Object,Collection,no +T1537,Transfer Data to Cloud Account,Exfiltration,no +T1526,Cloud Service Discovery,Discovery,no +T1527,Application Access Token,Defense Evasion|Lateral Movement,APT28 +T1514,Elevated Execution with Prompt,Privilege Escalation,no +T1505,Server Software Component,Persistence,no +T1503,Credentials from Web Browsers,Credential Access,TA505|Stolen Pencil|MuddyWater +T1504,PowerShell Profile,Persistence|Privilege Escalation,Turla +T1502,Parent PID Spoofing,Defense Evasion|Privilege Escalation,no +T1500,Compile After Delivery,Defense Evasion,MuddyWater +T1501,Systemd Service,Persistence,no +T1499,Endpoint Denial of Service,Impact,no +T1497,Virtualization/Sandbox Evasion,Defense Evasion|Discovery,The White Company|FIN7 +T1498,Network Denial of Service,Impact,no +T1496,Resource Hijacking,Impact,APT41|Lazarus Group +T1495,Firmware Corruption,Impact,no +T1494,Runtime Data Manipulation,Impact,APT38 +T1493,Transmitted Data Manipulation,Impact,APT38 +T1492,Stored Data Manipulation,Impact,FIN4|APT38 +T1491,Defacement,Impact,no +T1490,Inhibit System Recovery,Impact,no +T1489,Service Stop,Impact,Lazarus Group +T1488,Disk Content Wipe,Impact,Lazarus Group +T1487,Disk Structure Wipe,Impact,Lazarus Group|APT38|APT37 +T1486,Data Encrypted for Impact,Impact,APT41|TA505|APT38 +T1485,Data Destruction,Impact,Lazarus Group|APT38 +T1484,Group Policy Modification,Defense Evasion,no +T1483,Domain Generation Algorithms,Command And Control,APT41 +T1482,Domain Trust Discovery,Discovery,no +T1480,Execution Guardrails,Defense Evasion,APT33|Equation +T1223,Compiled HTML File,Defense Evasion|Execution,APT41|Silence|Lazarus Group|Dark Caracal|OilRig +T1222,File and Directory Permissions Modification,Defense Evasion,APT32 +T1220,XSL Script Processing,Defense Evasion|Execution,Cobalt Group +T1221,Template Injection,Defense Evasion,APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus +T1197,BITS Jobs,Defense Evasion|Persistence,Leviathan +T1191,CMSTP,Defense Evasion|Execution,Cobalt Group|MuddyWater +T1196,Control Panel Items,Defense Evasion|Execution,no +T1214,Credentials in Registry,Credential Access,Soft Cell +T1207,DCShadow,Defense Evasion,no +T1213,Data from Information Repositories,Collection,Ke3chang|APT28 +T1212,Exploitation for Credential Access,Credential Access,no +T1217,Browser Bookmark Discovery,Discovery,no +T1190,Exploit Public-Facing Application,Initial Access,Soft Cell|Night Dragon|Axiom +T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28 +T1200,Hardware Additions,Initial Access,no +T1189,Drive-by Compromise,Initial Access,Darkhotel|APT38|Lazarus Group|Dragonfly 2.0|BRONZE BUTLER|Leafminer|APT19|Dark Caracal|Threat Group-3390|APT32|Elderwood|Patchwork|APT37|PLATINUM +T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 +T1203,Exploitation for Client Execution,Execution,APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT37|APT29|Patchwork|Leviathan|TA459|Elderwood +T1208,Kerberoasting,Credential Access,no +T1215,Kernel Modules and Extensions,Persistence,no +T1201,Password Policy Discovery,Discovery,OilRig +T1205,Port Knocking,Defense Evasion|Persistence|Command And Control,no +T1198,SIP and Trust Provider Hijacking,Defense Evasion|Persistence,no +T1218,Signed Binary Proxy Execution,Defense Evasion|Execution,TA505|Rancor|Cobalt Group +T1202,Indirect Command Execution,Defense Evasion,no +T1194,Spearphishing via Service,Initial Access,FIN6|OilRig|Dark Caracal|Magic Hound +T1195,Supply Chain Compromise,Initial Access,APT41|Elderwood +T1219,Remote Access Tools,Command And Control,Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak +T1216,Signed Script Proxy Execution,Defense Evasion|Execution,APT32 +T1193,Spearphishing Attachment,Initial Access,APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Gorgon Group|Rancor|DarkHydrus|Lazarus Group|Cobalt Group|OilRig|APT19|FIN7|BRONZE BUTLER|Dragonfly 2.0|APT32|FIN8|MuddyWater|APT28|TA459|Elderwood|APT29|APT37|Patchwork|Leviathan|menuPass|Magic Hound|PLATINUM +T1209,Time Providers,Persistence,no +T1204,User Execution,Execution,Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Night Dragon|Darkhotel|Gallmaker|Dragonfly 2.0|APT33|APT19|BRONZE BUTLER|Dark Caracal|Cobalt Group|FIN7|DarkHydrus|Turla|Gorgon Group|OilRig|MuddyWater|Patchwork|Lazarus Group|APT32|Rancor|APT37|APT28|APT29|menuPass|FIN8|TA459|Elderwood|Leviathan|Magic Hound|PLATINUM +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|APT29|Leviathan|Patchwork|Magic Hound|FIN8 +T1206,Sudo Caching,Privilege Escalation,no +T1199,Trusted Relationship,Initial Access,APT28|menuPass +T1182,AppCert DLLs,Persistence|Privilege Escalation,Honeybee +T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil +T1175,Component Object Model and Distributed COM,Lateral Movement|Execution,MuddyWater +T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no +T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM +T1172,Domain Fronting,Command And Control,APT29 +T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 +T1173,Dynamic Data Exchange,Execution,TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 +T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 +T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no +T1177,LSASS Driver,Execution|Persistence,no +T1174,Password Filter DLL,Credential Access,no +T1180,Screensaver,Persistence,no +T1183,Image File Execution Options Injection,Privilege Escalation|Persistence|Defense Evasion,TEMP.Veles +T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 +T1184,SSH Hijacking,Lateral Movement,no +T1185,Man in the Browser,Collection,no +T1186,Process Doppelgänging,Defense Evasion,no +T1178,SID-History Injection,Privilege Escalation,no +T1156,.bash_profile and .bashrc,Persistence,no +T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,Turla|Lazarus Group|APT28 +T1155,AppleScript,Execution|Lateral Movement,no +T1138,Application Shimming,Persistence|Privilege Escalation,FIN7 +T1139,Bash History,Credential Access,no +T1146,Clear Command History,Defense Evasion,APT41 +T1136,Create Account,Persistence,APT41|Soft Cell|Dragonfly 2.0|Leafminer|APT3 +T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|Gorgon Group|Honeybee|menuPass|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER +T1157,Dylib Hijacking,Persistence|Privilege Escalation,no +T1148,HISTCONTROL,Defense Evasion,no +T1147,Hidden Users,Defense Evasion,no +T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1144,Gatekeeper Bypass,Defense Evasion,no +T1158,Hidden Files and Directories,Defense Evasion|Persistence,APT32|Tropic Trooper|APT28|Lazarus Group +T1149,LC_MAIN Hijacking,Defense Evasion,no +T1152,Launchctl,Defense Evasion|Execution|Persistence,no +T1168,Local Job Scheduling,Persistence|Execution,no +T1141,Input Prompt,Credential Access,FIN4 +T1162,Login Item,Persistence,no +T1137,Office Application Startup,Persistence,APT32|APT28 +T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no +T1145,Private Keys,Credential Access,no +T1163,Rc.common,Persistence,no +T1142,Keychain,Credential Access,no +T1159,Launch Agent,Persistence,no +T1151,Space after Filename,Defense Evasion|Execution,no +T1161,LC_LOAD_DYLIB Addition,Persistence,no +T1160,Launch Daemon,Persistence|Privilege Escalation,no +T1153,Source,Execution,no +T1154,Trap,Execution|Persistence,no +T1135,Network Share Discovery,Discovery,APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug +T1164,Re-opened Applications,Persistence,no +T1169,Sudo,Privilege Escalation,no +T1167,Securityd Memory,Credential Access,no +T1166,Setuid and Setgid,Privilege Escalation|Persistence,no +T1165,Startup Items,Persistence|Privilege Escalation,no +T1133,External Remote Services,Persistence|Initial Access,APT41|Soft Cell|TEMP.Veles|Night Dragon|OilRig|Ke3chang|Dragonfly 2.0|FIN5|Threat Group-3390|APT18 +T1132,Data Encoding,Command And Control,APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork +T1131,Authentication Package,Persistence,no +T1130,Install Root Certificate,Defense Evasion,no +T1129,Execution through Module Load,Execution,no +T1128,Netsh Helper DLL,Persistence,no +T1127,Trusted Developer Utilities,Defense Evasion|Execution,no +T1126,Network Share Connection Removal,Defense Evasion,Threat Group-3390 +T1125,Video Capture,Collection,Silence|FIN7 +T1124,System Time Discovery,Discovery,The White Company|Lazarus Group|BRONZE BUTLER|Turla +T1123,Audio Capture,Collection,APT37 +T1122,Component Object Model Hijacking,Defense Evasion|Persistence,APT28 +T1121,Regsvcs/Regasm,Defense Evasion|Execution,no +T1120,Peripheral Device Discovery,Discovery,APT37|Gamaredon Group|Equation|APT28 +T1119,Automated Collection,Collection,APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6 +T1118,InstallUtil,Defense Evasion|Execution,no +T1117,Regsvr32,Defense Evasion|Execution,WIRTE|APT19|Cobalt Group|Leviathan|APT32|Deep Panda +T1116,Code Signing,Defense Evasion,APT41|FIN6|TA505|FIN7|Honeybee|APT37|Leviathan|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel +T1115,Clipboard Data,Collection,APT38 +T1114,Email Collection,Collection,FIN4|Dragonfly 2.0|APT28|Magic Hound|Ke3chang|Leafminer|APT1 +T1113,Screen Capture,Collection,Silence|MuddyWater|OilRig|Dragonfly 2.0|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28 +T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|Threat Group-3390|Patchwork|APT19|Honeybee|Gorgon Group|FIN8 +T1111,Two-Factor Authentication Interception,Credential Access,no +T1110,Brute Force,Credential Access,APT41|APT33|Leafminer|OilRig|Dragonfly 2.0|APT3|Lazarus Group|Turla +T1109,Component Firmware,Defense Evasion|Persistence,Equation +T1108,Redundant Access,Defense Evasion|Persistence,Stolen Pencil|Cobalt Group|Leafminer|APT3|FIN5|OilRig|Threat Group-3390 +T1107,File Deletion,Defense Evasion,APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Honeybee|Patchwork|Dragonfly 2.0|menuPass|Cobalt Group|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|FIN10|Threat Group-3390|APT28|Group5|Lazarus Group|APT18|APT29 +T1106,Execution through API,Execution,Turla|Silence|APT37|Gorgon Group +T1105,Remote File Copy,Command And Control|Lateral Movement,Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Turla|Rancor|Gorgon Group|Cobalt Group|Dragonfly 2.0|OilRig|APT37|FIN8|PLATINUM|Leviathan|Elderwood|Magic Hound|APT3|APT32|BRONZE BUTLER|FIN7|FIN10|menuPass|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28 +T1104,Multi-Stage Channels,Command And Control,MuddyWater|APT3 +T1103,AppInit DLLs,Persistence|Privilege Escalation,no +T1102,Web Service,Command And Control|Defense Evasion,APT41|APT12|FIN6|Turla|FIN7|BRONZE BUTLER|Leviathan|APT37|Magic Hound|RTM|Patchwork|Carbanak +T1101,Security Support Provider,Persistence,no +T1100,Web Shell,Persistence|Privilege Escalation,Soft Cell|Threat Group-3390|TEMP.Veles|Leviathan|APT39|Dragonfly 2.0|APT32|OilRig|Deep Panda +T1099,Timestomp,Defense Evasion,TEMP.Veles|APT32|Lazarus Group|APT28 +T1098,Account Manipulation,Credential Access|Persistence,Magic Hound|Dragonfly 2.0|APT3|Lazarus Group +T1097,Pass the Ticket,Lateral Movement,APT32|Ke3chang|BRONZE BUTLER|APT29 +T1096,NTFS File Attributes,Defense Evasion,APT32 +T1095,Standard Non-Application Layer Protocol,Command And Control,APT29|PLATINUM|APT3 +T1094,Custom Command and Control Protocol,Command And Control,PLATINUM|APT37|OilRig|APT32 +T1093,Process Hollowing,Defense Evasion,menuPass|Gorgon Group|Patchwork +T1092,Communication Through Removable Media,Command And Control,APT28 +T1091,Replication Through Removable Media,Lateral Movement|Initial Access,Darkhotel|APT28 +T1090,Connection Proxy,Command And Control|Defense Evasion,APT41|Soft Cell|Turla|APT39|MuddyWater|APT3|Lazarus Group|menuPass|Strider|APT28 +T1089,Disabling Security Tools,Defense Evasion,Kimsuky|Turla|Night Dragon|Dragonfly 2.0|Gorgon Group|Threat Group-3390|Lazarus Group|Putter Panda|Carbanak +T1088,Bypass User Account Control,Defense Evasion|Privilege Escalation,APT37|MuddyWater|Honeybee|Threat Group-3390|Cobalt Group|BRONZE BUTLER|Patchwork|APT29 +T1087,Account Discovery,Discovery,APT32|APT1|Dragonfly 2.0|BRONZE BUTLER|OilRig|Threat Group-3390|menuPass|FIN6|Poseidon Group|APT3|admin@338|Ke3chang +T1086,PowerShell,Execution,APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|DarkHydrus|APT19|APT28|Thrip|Dragonfly 2.0|Cobalt Group|Gorgon Group|Leviathan|TA459|MuddyWater|FIN8|CopyKittens|OilRig|Magic Hound|BRONZE BUTLER|APT32|FIN10|FIN7|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda +T1085,Rundll32,Defense Evasion|Execution,TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28 +T1084,Windows Management Instrumentation Event Subscription,Persistence,Turla|Leviathan|APT29 +T1083,File and Directory Discovery,Discovery,Kimsuky|APT32|MuddyWater|APT18|Leafminer|Dragonfly 2.0|Honeybee|Dark Caracal|Magic Hound|APT3|Sowbug|BRONZE BUTLER|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang +T1082,System Information Discovery,Discovery,Kimsuky|Tropic Trooper|Darkhotel|MuddyWater|APT18|APT37|Honeybee|APT19|APT32|Magic Hound|OilRig|APT3|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang +T1081,Credentials in Files,Credential Access,OilRig|Kimsuky|Turla|TA505|Stolen Pencil|MuddyWater|APT3 +T1080,Taint Shared Content,Lateral Movement,Darkhotel +T1079,Multilayer Encryption,Command And Control,no +T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT41|Soft Cell|TEMP.Veles|APT39|Stolen Pencil|FIN4|Night Dragon|Dragonfly 2.0|FIN8|Leviathan|APT33|APT3|FIN5|OilRig|menuPass|APT28|FIN10|APT32|Suckfly|FIN6|Threat Group-1314|Threat Group-3390|APT18|PittyTiger|Carbanak +T1077,Windows Admin Shares,Lateral Movement,APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang +T1076,Remote Desktop Protocol,Lateral Movement,APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|menuPass|FIN10|Patchwork|FIN6|Lazarus Group|APT1|Axiom +T1075,Pass the Hash,Lateral Movement,Soft Cell|APT32|Night Dragon|APT28|APT1 +T1074,Data Staged,Collection,Machete|Soft Cell|TEMP.Veles|Night Dragon|Honeybee|Patchwork|Dragonfly 2.0|Leviathan|FIN8|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28 +T1073,DLL Side-Loading,Defense Evasion,APT41|Soft Cell|Tropic Trooper|Patchwork|APT19|APT32|APT3|menuPass|Threat Group-3390 +T1072,Third-party Software,Execution|Lateral Movement,Threat Group-1314 +T1071,Standard Application Layer Protocol,Command And Control,APT41|Machete|WIRTE|APT33|FIN4|Night Dragon|APT18|SilverTerrier|APT38|Dragonfly 2.0|APT19|Cobalt Group|FIN7|Threat Group-3390|Turla|APT37|Rancor|Orangeworm|Honeybee|Ke3chang|Dark Caracal|Lazarus Group|BRONZE BUTLER|APT32|OilRig|Magic Hound|Gamaredon Group|Stealth Falcon|FIN6|APT28 +T1070,Indicator Removal on Host,Defense Evasion,APT41|APT29|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28 +T1069,Permission Groups Discovery,Discovery,FIN6|Dragonfly 2.0|OilRig|APT3|admin@338|Ke3chang +T1068,Exploitation for Privilege Escalation,Privilege Escalation,APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28 +T1067,Bootkit,Persistence,APT41|Lazarus Group|APT28 +T1066,Indicator Removal from Tools,Defense Evasion,Soft Cell|TEMP.Veles|Patchwork|APT3|Turla|OilRig|Deep Panda +T1065,Uncommonly Used Port,Command And Control,TEMP.Veles|APT33|APT32|Gorgon Group|Magic Hound|Group5|Lazarus Group|APT3 +T1064,Scripting,Defense Evasion|Execution,Machete|Turla|TA505|Silence|WIRTE|APT39|FIN4|APT32|Darkhotel|Gallmaker|Dark Caracal|Lazarus Group|menuPass|APT19|Dragonfly 2.0|Leafminer|Rancor|Honeybee|APT37|Ke3chang|Cobalt Group|Patchwork|FIN7|Gorgon Group|MuddyWater|Leviathan|FIN8|TA459|APT28|Magic Hound|OilRig|FIN5|BRONZE BUTLER|FIN10|Gamaredon Group|Stealth Falcon|FIN6|APT3|APT29|Deep Panda|APT1 +T1063,Security Software Discovery,Discovery,The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon +T1062,Hypervisor,Persistence,no +T1061,Graphical User Interface,Execution,APT3 +T1060,Registry Run Keys / Startup Folder,Persistence,APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|APT19|Dark Caracal|Ke3chang|Dragonfly 2.0|Cobalt Group|Honeybee|Threat Group-3390|Gorgon Group|MuddyWater|APT37|Leviathan|BRONZE BUTLER|APT3|Magic Hound|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel +T1059,Command-Line Interface,Execution,APT41|Soft Cell|Turla|Silence|APT32|Cobalt Group|MuddyWater|APT18|APT38|Dragonfly 2.0|Gorgon Group|APT28|FIN7|Rancor|Honeybee|APT37|Leviathan|FIN8|Magic Hound|Sowbug|OilRig|BRONZE BUTLER|menuPass|Threat Group-3390|Suckfly|Patchwork|Lazarus Group|Threat Group-1314|APT3|admin@338|APT1|Ke3chang +T1058,Service Registry Permissions Weakness,Persistence|Privilege Escalation,no +T1057,Process Discovery,Discovery,Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang +T1056,Input Capture,Collection|Credential Access,APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28 +T1055,Process Injection,Defense Evasion|Privilege Escalation,APT41|Kimsuky|Tropic Trooper|Gorgon Group|Turla|Threat Group-3390|APT37|Cobalt Group|Honeybee|Lazarus Group|PLATINUM|Putter Panda +T1054,Indicator Blocking,Defense Evasion,no +T1053,Scheduled Task,Execution|Persistence|Privilege Escalation,APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|Dragonfly 2.0|OilRig|Rancor|Patchwork|FIN8|BRONZE BUTLER|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|Threat Group-3390|APT18|APT3|APT29 +T1052,Exfiltration Over Physical Medium,Exfiltration,no +T1051,Shared Webroot,Lateral Movement,no +T1050,New Service,Persistence|Privilege Escalation,Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|FIN7|APT32|Threat Group-3390|APT3|Lazarus Group|Carbanak +T1049,System Network Connections Discovery,Discovery,APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang +T1048,Exfiltration Over Alternative Protocol,Exfiltration,Turla|APT33|Thrip|FIN8|OilRig|Lazarus Group +T1047,Windows Management Instrumentation,Execution,APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|Leviathan|FIN8|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda +T1046,Network Service Scanning,Discovery,APT41|Tropic Trooper|APT39|APT32|Leafminer|Cobalt Group|OilRig|menuPass|Suckfly|FIN6|Threat Group-3390 +T1045,Software Packing,Defense Evasion,Soft Cell|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Group5|Patchwork|APT29|Night Dragon +T1044,File System Permissions Weakness,Persistence|Privilege Escalation,no +T1043,Commonly Used Port,Command And Control,Machete|OilRig|APT28|TEMP.Veles|APT33|APT32|Night Dragon|APT29|APT18|Tropic Trooper|APT19|FIN7|Dragonfly 2.0|FIN8|APT37|Magic Hound|APT3|Lazarus Group|Threat Group-3390 +T1042,Change Default File Association,Persistence,Kimsuky +T1041,Exfiltration Over Command and Control Channel,Exfiltration,Kimsuky|Soft Cell|APT32|APT3|Gamaredon Group|Stealth Falcon|Lazarus Group|Ke3chang +T1040,Network Sniffing,Credential Access|Discovery,APT33|Stolen Pencil|APT28 +T1039,Data from Network Shared Drive,Collection,Sowbug|BRONZE BUTLER|menuPass +T1038,DLL Search Order Hijacking,Persistence|Privilege Escalation|Defense Evasion,Threat Group-3390|menuPass +T1037,Logon Scripts,Lateral Movement|Persistence,Cobalt Group|APT28 +T1036,Masquerading,Defense Evasion,APT41|Soft Cell|PLATINUM|Ke3chang|Scarlet Mimic|menuPass|FIN6|TEMP.Veles|Dragonfly 2.0|MuddyWater|BRONZE BUTLER|Sowbug|FIN7|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1 +T1035,Service Execution,Execution,Silence|FIN6|APT32|Honeybee|Ke3chang +T1034,Path Interception,Persistence|Privilege Escalation,no +T1033,System Owner/User Discovery,Discovery,APT41|Soft Cell|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|Magic Hound|OilRig|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3 +T1032,Standard Cryptographic Protocol,Command And Control,Machete|APT33|Tropic Trooper|Cobalt Group|OilRig|FIN8|BRONZE BUTLER|Stealth Falcon|FIN6|Lazarus Group|Taidoor +T1031,Modify Existing Service,Persistence,APT41|APT32|Honeybee|APT19 +T1030,Data Transfer Size Limits,Exfiltration,Threat Group-3390 +T1029,Scheduled Transfer,Exfiltration,no +T1028,Windows Remote Management,Execution|Lateral Movement,Threat Group-3390 +T1027,Obfuscated Files or Information,Defense Evasion,Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|menuPass|Patchwork|Leafminer|Cobalt Group|APT37|Threat Group-3390|Honeybee|Dark Caracal|APT19|FIN8|BlackOasis|Elderwood|Leviathan|MuddyWater|FIN7|Magic Hound|APT3|OilRig|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28 +T1026,Multiband Communication,Command And Control,Lazarus Group +T1025,Data from Removable Media,Collection,Machete|Turla|Gamaredon Group|APT28 +T1024,Custom Cryptographic Protocol,Command And Control,APT28|BRONZE BUTLER|Lazarus Group +T1023,Shortcut Modification,Persistence,APT39|Darkhotel|APT29|FIN7|Gorgon Group|Dragonfly 2.0|Leviathan|Lazarus Group +T1022,Data Encrypted,Exfiltration,Kimsuky|Soft Cell|Turla|menuPass|APT32|Patchwork|Honeybee|CopyKittens|BRONZE BUTLER|FIN6|Lazarus Group|Threat Group-3390|Ke3chang +T1021,Remote Services,Lateral Movement,TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN +T1020,Automated Exfiltration,Exfiltration,Honeybee +T1019,System Firmware,Persistence,no +T1018,Remote System Discovery,Discovery,Soft Cell|APT32|Threat Group-3390|Dragonfly 2.0|Deep Panda|Ke3chang|Leafminer|FIN8|APT3|FIN5|BRONZE BUTLER|menuPass|FIN6|Turla +T1017,Application Deployment Software,Lateral Movement,APT32 +T1016,System Network Configuration Discovery,Discovery,APT41|Soft Cell|APT39|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|OilRig|Magic Hound|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang +T1015,Accessibility Features,Persistence|Privilege Escalation,APT41|APT3|APT29|Deep Panda|Axiom +T1014,Rootkit,Defense Evasion,APT41|APT28|Winnti Group +T1013,Port Monitors,Persistence|Privilege Escalation,no +T1012,Query Registry,Discovery,APT32|Dragonfly 2.0|Threat Group-3390|OilRig|Stealth Falcon|Lazarus Group|Turla +T1011,Exfiltration Over Other Network Medium,Exfiltration,no +T1010,Application Window Discovery,Discovery,Lazarus Group +T1009,Binary Padding,Defense Evasion,Patchwork|APT32|Leviathan|BRONZE BUTLER|Moafee +T1008,Fallback Channels,Command And Control,APT41|OilRig|Lazarus Group +T1007,System Service Discovery,Discovery,APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang +T1006,File System Logical Offsets,Defense Evasion,no +T1005,Data from Local System,Collection,Kimsuky|Soft Cell|Turla|menuPass|Dragonfly 2.0|Dark Caracal|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang +T1004,Winlogon Helper DLL,Persistence,Tropic Trooper|Turla +T1003,Credential Dumping,Credential Access,APT41|Soft Cell|TEMP.Veles|APT33|Leviathan|APT39|Stolen Pencil|APT32|Night Dragon|Dragonfly 2.0|Leafminer|Lazarus Group|Magic Hound|APT37|MuddyWater|PLATINUM|FIN8|Sowbug|BRONZE BUTLER|FIN5|OilRig|menuPass|Strider|Patchwork|Stealth Falcon|Suckfly|FIN6|Poseidon Group|Threat Group-3390|APT3|Molerats|APT28|APT1|Ke3chang|Cleaver|Axiom +T1002,Data Compressed,Exfiltration,APT41|Soft Cell|Gallmaker|APT33|APT32|APT39|MuddyWater|Honeybee|Magic Hound|APT28|Dragonfly 2.0|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT1|Ke3chang +T1001,Data Obfuscation,Command And Control,APT28|Axiom diff --git a/macros/access_lsass_memory_for_dump_creation_filter.yml b/macros/access_lsass_memory_for_dump_creation_filter.yml deleted file mode 100644 index ed58de5848..0000000000 --- a/macros/access_lsass_memory_for_dump_creation_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for access lsass memory for dump creation -name: access_lsass_memory_for_dump_creation_filter diff --git a/macros/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter.yml b/macros/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter.yml deleted file mode 100644 index 701f306f47..0000000000 --- a/macros/attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for attempt to set default powershell execution policy to unrestricted or bypass -name: attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter diff --git a/macros/attempted_credential_dump_from_registry_via_reg_filter.yml b/macros/attempted_credential_dump_from_registry_via_reg_filter.yml deleted file mode 100644 index 9986f4c8a7..0000000000 --- a/macros/attempted_credential_dump_from_registry_via_reg_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for attempted credential dump from registry via reg -name: attempted_credential_dump_from_registry_via_reg_filter diff --git a/macros/attrib_to_hide_files_filter.yml b/macros/attrib_to_hide_files_filter.yml deleted file mode 100644 index 7151ae3088..0000000000 --- a/macros/attrib_to_hide_files_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: attrib_to_hide_files_filter \ No newline at end of file diff --git a/macros/aws_cloudwatchlogs_eks.yml b/macros/aws_cloudwatchlogs_eks.yml new file mode 100644 index 0000000000..712770fab9 --- /dev/null +++ b/macros/aws_cloudwatchlogs_eks.yml @@ -0,0 +1,4 @@ +definition: sourcetype="aws:cloudwatchlogs:eks" +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: aws_cloudwatchlogs_eks diff --git a/macros/aws_s3_accesslogs.yml b/macros/aws_s3_accesslogs.yml new file mode 100644 index 0000000000..60aecdb081 --- /dev/null +++ b/macros/aws_s3_accesslogs.yml @@ -0,0 +1,4 @@ +definition: sourcetype=aws:s3:accesslogs +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: aws_s3_accesslogs diff --git a/macros/change_file_association_filter.yml b/macros/change_file_association_filter.yml deleted file mode 100644 index 4b55678675..0000000000 --- a/macros/change_file_association_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ - -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: change_file_association_filter \ No newline at end of file diff --git a/macros/children_of_spoolsv_filter.yml b/macros/children_of_spoolsv_filter.yml deleted file mode 100644 index c7a8f0f943..0000000000 --- a/macros/children_of_spoolsv_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: children_of_spoolsv_filter \ No newline at end of file diff --git a/macros/cloudtrail.yml b/macros/cloudtrail.yml new file mode 100644 index 0000000000..bb4982174e --- /dev/null +++ b/macros/cloudtrail.yml @@ -0,0 +1,4 @@ +definition: sourcetype=aws:cloudtrail +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: cloudtrail diff --git a/macros/cloudwatch_eks.yml b/macros/cloudwatch_eks.yml new file mode 100644 index 0000000000..5a8dcca164 --- /dev/null +++ b/macros/cloudwatch_eks.yml @@ -0,0 +1,3 @@ +definition: sourcetype="aws:cloudwatchlogs:eks" +description: customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch eks logs. Replace the macro definition with configurations for your Splunk Environmnent. +name: cloudwatch_eks diff --git a/macros/cloudwatch_vpc.yml b/macros/cloudwatch_vpc.yml new file mode 100644 index 0000000000..1c8bd5d11b --- /dev/null +++ b/macros/cloudwatch_vpc.yml @@ -0,0 +1,3 @@ +definition: sourcetype=aws:cloudwatchlogs:vpcflow +description: customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch vpc logs. Replace the macro definition with configurations for your Splunk Environmnent. +name: cloudwatch_vpc diff --git a/macros/cloudwatchlogs_vpcflow.yml b/macros/cloudwatchlogs_vpcflow.yml new file mode 100644 index 0000000000..d9dd3ac720 --- /dev/null +++ b/macros/cloudwatchlogs_vpcflow.yml @@ -0,0 +1,4 @@ +definition: sourcetype=aws:cloudwatchlogs:vpcflow +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: cloudwatchlogs_vpcflow diff --git a/macros/common_ransomware_extensions_filter.yml b/macros/common_ransomware_extensions_filter.yml deleted file mode 100644 index 6a450e8a58..0000000000 --- a/macros/common_ransomware_extensions_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: common_ransomware_extensions_filter \ No newline at end of file diff --git a/macros/common_ransomware_notes_filter.yml b/macros/common_ransomware_notes_filter.yml deleted file mode 100644 index ef33343820..0000000000 --- a/macros/common_ransomware_notes_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: common_ransomware_notes_filter \ No newline at end of file diff --git a/macros/container_implant_aws_detection_filter.yml b/macros/container_implant_aws_detection_filter.yml deleted file mode 100644 index 3d37856762..0000000000 --- a/macros/container_implant_aws_detection_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: container_implant_aws_detection_filter diff --git a/macros/container_implant_gcp_detection_filter.yml b/macros/container_implant_gcp_detection_filter.yml deleted file mode 100644 index d25f44bd6a..0000000000 --- a/macros/container_implant_gcp_detection_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: container_implant_gcp_detection_filter diff --git a/macros/create_local_admin_via_net_filter.yml b/macros/create_local_admin_via_net_filter.yml deleted file mode 100644 index e3a215d554..0000000000 --- a/macros/create_local_admin_via_net_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: create_local_admin_via_net_filter \ No newline at end of file diff --git a/macros/create_remote_thread_into_lsass_filter.yml b/macros/create_remote_thread_into_lsass_filter.yml deleted file mode 100644 index 893485aba0..0000000000 --- a/macros/create_remote_thread_into_lsass_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for create remote thread into lsass -name: create_remote_thread_into_lsass_filter diff --git a/macros/creation_of_shadow_copy_filter.yml b/macros/creation_of_shadow_copy_filter.yml deleted file mode 100644 index 10e3b8e032..0000000000 --- a/macros/creation_of_shadow_copy_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for creation of shadow copy -name: creation_of_shadow_copy_filter diff --git a/macros/creation_of_shadow_copy_with_wmic_and_powershell_filter.yml b/macros/creation_of_shadow_copy_with_wmic_and_powershell_filter.yml deleted file mode 100644 index 3da66555bb..0000000000 --- a/macros/creation_of_shadow_copy_with_wmic_and_powershell_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for creation of shadow copy with wmic and powershell -name: creation_of_shadow_copy_with_wmic_and_powershell_filter diff --git a/macros/cred_dump_via_copy_from_shadowcopy_filter.yml b/macros/cred_dump_via_copy_from_shadowcopy_filter.yml deleted file mode 100644 index 47086687a7..0000000000 --- a/macros/cred_dump_via_copy_from_shadowcopy_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for cred dump via copy from shadowcopy -name: cred_dump_via_copy_from_shadowcopy_filter diff --git a/macros/cred_dump_via_symlink_shadowcopy_filter.yml b/macros/cred_dump_via_symlink_shadowcopy_filter.yml deleted file mode 100644 index 97ea55fdb8..0000000000 --- a/macros/cred_dump_via_symlink_shadowcopy_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for cred dump via symlink shadowcopy -name: cred_dump_via_symlink_shadowcopy_filter diff --git a/macros/detect_activity_related_to_pass_the_hash_attacks_filter.yml b/macros/detect_activity_related_to_pass_the_hash_attacks_filter.yml deleted file mode 100644 index 083bce747e..0000000000 --- a/macros/detect_activity_related_to_pass_the_hash_attacks_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for detect activity related to pass the hash attacks -name: detect_activity_related_to_pass_the_hash_attacks_filter diff --git a/macros/detect_credential_dumping_through_LSASS_access_filter.yml b/macros/detect_credential_dumping_through_LSASS_access_filter.yml deleted file mode 100644 index 956bb0d6a4..0000000000 --- a/macros/detect_credential_dumping_through_LSASS_access_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for detect credential dumping through LSASS access -name: detect_credential_dumping_through_LSASS_access_filter diff --git a/macros/detect_mimikatz_using_loaded_images_filter.yml b/macros/detect_mimikatz_using_loaded_images_filter.yml deleted file mode 100644 index 5b0be46ce2..0000000000 --- a/macros/detect_mimikatz_using_loaded_images_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for detect mimikatz using loaded images -name: detect_mimikatz_using_loaded_images_filter diff --git a/macros/dns_query_length_mltk_filter.yml b/macros/dns_query_length_mltk_filter.yml deleted file mode 100644 index 90b9429a0d..0000000000 --- a/macros/dns_query_length_mltk_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -description: Use this macro to add additional filters for monitoring DNS query length using MLTK -name: dns_query_length_mltk_filter -definition: search * diff --git a/macros/dns_query_length_with_high_standard_deviation_filter.yml b/macros/dns_query_length_with_high_standard_deviation_filter.yml deleted file mode 100644 index e88b8382eb..0000000000 --- a/macros/dns_query_length_with_high_standard_deviation_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -description: Use this macro to add additional filters for monitoring DNS query length with high standard deviation -name: dns_query_length_with_high_standard_deviation_filter -definition: search * diff --git a/macros/dns_record_changed_filter.yml b/macros/dns_record_changed_filter.yml deleted file mode 100644 index 8bf9820083..0000000000 --- a/macros/dns_record_changed_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -description: Use this macro to add additional filters for monitoring DNS records changed -name: dns_record_changed_filter -definition: search * diff --git a/macros/dragonfly_schtasks_filter.yml b/macros/dragonfly_schtasks_filter.yml deleted file mode 100644 index c5dfd45827..0000000000 --- a/macros/dragonfly_schtasks_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: dragonfly_schtasks_filter \ No newline at end of file diff --git a/macros/dump_lsass_via_comsvcs_dll_filter.yml b/macros/dump_lsass_via_comsvcs_dll_filter.yml deleted file mode 100644 index 152135df5e..0000000000 --- a/macros/dump_lsass_via_comsvcs_dll_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for dump lsass via comsvcs dll filter -name: dump_lsass_via_comsvcs_dll_filter diff --git a/macros/ec2_excessive_runinstances_mltk_input_filter.yml b/macros/ec2_excessive_runinstances_mltk_input_filter.yml deleted file mode 100644 index a48a27892c..0000000000 --- a/macros/ec2_excessive_runinstances_mltk_input_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring ec2 - runinstances [eg - src_user != 'someUserNameExperiencingFalsePositives']. -name: ec2_excessive_runinstances_mltk_input_filter diff --git a/macros/ec2_excessive_terminateinstances_mltk_input_filter.yml b/macros/ec2_excessive_terminateinstances_mltk_input_filter.yml deleted file mode 100644 index 0918ce133f..0000000000 --- a/macros/ec2_excessive_terminateinstances_mltk_input_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring ec2 - terminateinstances [eg - src_user != 'someUserNameExperiencingFalsePositives']. -name: ec2_excessive_terminateinstances_mltk_input_filter diff --git a/macros/email_files_not_in_outlook_directory_filter.yml b/macros/email_files_not_in_outlook_directory_filter.yml deleted file mode 100644 index 9f7fee2eba..0000000000 --- a/macros/email_files_not_in_outlook_directory_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters to exclude false positive results -name: email_files_not_in_outlook_directory_filter diff --git a/macros/file_write_spikes_filter.yml b/macros/file_write_spikes_filter.yml deleted file mode 100644 index b9ccc594b1..0000000000 --- a/macros/file_write_spikes_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: file_write_spikes_filter \ No newline at end of file diff --git a/macros/first_time_seen_cmd_line_filter.yml b/macros/first_time_seen_cmd_line_filter.yml deleted file mode 100644 index 8a4785e9b9..0000000000 --- a/macros/first_time_seen_cmd_line_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for first time seen command line, you can exclude legitimate parent_processes that spawn cmd.exe. Eg- seach parent_process_name != "foo" -name: first_time_seen_cmd_line_filter diff --git a/macros/google_gcp_pubnet_message.yml b/macros/google_gcp_pubnet_message.yml new file mode 100644 index 0000000000..e8de3e401e --- /dev/null +++ b/macros/google_gcp_pubnet_message.yml @@ -0,0 +1,3 @@ +definition: sourcetype="google:gcp:pubsub:message" +description: customer specific splunk configurations(eg- index, source, sourcetype) for Google GCP. Replace the macro definition with configurations for your Splunk Environmnent. +name: google_gcp_pubnet_message diff --git a/macros/google_gcp_pubsub_message.yml b/macros/google_gcp_pubsub_message.yml new file mode 100644 index 0000000000..e040e3cfa3 --- /dev/null +++ b/macros/google_gcp_pubsub_message.yml @@ -0,0 +1,4 @@ +definition: sourcetype="google:gcp:pubsub:message" +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: google_gcp_pubsub_message diff --git a/macros/kubernetes_aws_scan_fingerprint_detection_filter.yml b/macros/kubernetes_aws_scan_fingerprint_detection_filter.yml deleted file mode 100644 index 3f70da4899..0000000000 --- a/macros/kubernetes_aws_scan_fingerprint_detection_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_aws_scan_fingerprint_detection diff --git a/macros/kubernetes_gcp_scan_fingerprint_detection_filter.yml b/macros/kubernetes_gcp_scan_fingerprint_detection_filter.yml deleted file mode 100644 index fbee229bf7..0000000000 --- a/macros/kubernetes_gcp_scan_fingerprint_detection_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_gcp_scan_fingerprint_detection diff --git a/macros/kubernetes_pods_aws_scan_fingerprint_detection_filter.yml b/macros/kubernetes_pods_aws_scan_fingerprint_detection_filter.yml deleted file mode 100644 index 2156737cbe..0000000000 --- a/macros/kubernetes_pods_aws_scan_fingerprint_detection_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters -name: kubernetes_pods_aws_scan_fingerprint_detection diff --git a/macros/lnk_executing_a_process_filter.yml b/macros/lnk_executing_a_process_filter.yml deleted file mode 100644 index 346ce4fdca..0000000000 --- a/macros/lnk_executing_a_process_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: lnk_executing_a_process_filter \ No newline at end of file diff --git a/macros/malicious_powershell_process_obfuscation_techniques_output_filter.yml b/macros/malicious_powershell_process_obfuscation_techniques_output_filter.yml deleted file mode 100644 index 56fd9b8503..0000000000 --- a/macros/malicious_powershell_process_obfuscation_techniques_output_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add output filters for Malicious PowerShell - Process With Obfuscation Techniques. -name: malicious_powershell_process_obfuscation_techniques_output_filter diff --git a/macros/netbackup.yml b/macros/netbackup.yml new file mode 100644 index 0000000000..2021f88e1b --- /dev/null +++ b/macros/netbackup.yml @@ -0,0 +1,4 @@ +definition: sourcetype="netbackup_logs" +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: netbackup diff --git a/macros/okta.yml b/macros/okta.yml new file mode 100644 index 0000000000..933b1e6f2c --- /dev/null +++ b/macros/okta.yml @@ -0,0 +1,4 @@ +definition: eventtype=okta_log +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: okta diff --git a/macros/okta_failed_sso_attempt_filter.yml b/macros/okta_failed_sso_attempt_filter.yml deleted file mode 100644 index 1d62efcfe0..0000000000 --- a/macros/okta_failed_sso_attempt_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: okta_failed_sso_attempt_filter \ No newline at end of file diff --git a/macros/okta_multiple_users_with_auth_failures_from_ip_filter.yml b/macros/okta_multiple_users_with_auth_failures_from_ip_filter.yml deleted file mode 100644 index be4ef2b1d3..0000000000 --- a/macros/okta_multiple_users_with_auth_failures_from_ip_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: okta_multiple_users_with_auth_failures_from_ip_filter \ No newline at end of file diff --git a/macros/okta_user_lockouts_filter.yml b/macros/okta_user_lockouts_filter.yml deleted file mode 100644 index 27f4329ce0..0000000000 --- a/macros/okta_user_lockouts_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: okta_user_lockouts_filter \ No newline at end of file diff --git a/macros/okta_user_logins_from_multiple_cities_filter.yml b/macros/okta_user_logins_from_multiple_cities_filter.yml deleted file mode 100644 index 81b8ca5841..0000000000 --- a/macros/okta_user_logins_from_multiple_cities_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: okta_user_logins_from_multiple_cities_filter \ No newline at end of file diff --git a/macros/outbound_smb_connections_filter.yml b/macros/outbound_smb_connections_filter.yml deleted file mode 100644 index a402945c32..0000000000 --- a/macros/outbound_smb_connections_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for outbound SMB traffic detection -name: outbound_smb_connections_filter diff --git a/macros/outlook_writing_zip_filter.yml b/macros/outlook_writing_zip_filter.yml deleted file mode 100644 index fa70d5dfc0..0000000000 --- a/macros/outlook_writing_zip_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: outlook_writing_zip_filter \ No newline at end of file diff --git a/macros/previously_seen_cloud_compute_creations_by_user_input_filter.yml b/macros/previously_seen_cloud_compute_creations_by_user_input_filter.yml deleted file mode 100644 index ca8d553a3e..0000000000 --- a/macros/previously_seen_cloud_compute_creations_by_user_input_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring users that create - cloud compute images -name: previously_seen_cloud_compute_creations_by_user_input_filter diff --git a/macros/previously_seen_cloud_compute_image_input_filter.yml b/macros/previously_seen_cloud_compute_image_input_filter.yml deleted file mode 100644 index 321082ee44..0000000000 --- a/macros/previously_seen_cloud_compute_image_input_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring cloud compute - images -name: previously_seen_cloud_compute_image_input_filter diff --git a/macros/previously_seen_cloud_compute_instance_types_input_filter.yml b/macros/previously_seen_cloud_compute_instance_types_input_filter.yml deleted file mode 100644 index fa12cab54b..0000000000 --- a/macros/previously_seen_cloud_compute_instance_types_input_filter.yml +++ /dev/null @@ -1,4 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring cloud compute - instance types -name: previously_seen_cloud_compute_instance_types_input_filter diff --git a/macros/previously_seen_cloud_regions_input_filter.yml b/macros/previously_seen_cloud_regions_input_filter.yml deleted file mode 100644 index 70906c867e..0000000000 --- a/macros/previously_seen_cloud_regions_input_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for monitoring your cloud regions -name: previously_seen_cloud_regions_input_filter diff --git a/macros/rare_executables_on_endpoint_filter.yml b/macros/rare_executables_on_endpoint_filter.yml deleted file mode 100644 index 816438def9..0000000000 --- a/macros/rare_executables_on_endpoint_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to filter out false positives -name: rare_executables_on_endpoint_filter diff --git a/macros/remote_desktop_network_traffic_filter.yml b/macros/remote_desktop_network_traffic_filter.yml deleted file mode 100644 index 2f2c9149f8..0000000000 --- a/macros/remote_desktop_network_traffic_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for remote desktop network traffic -name: remote_desktop_network_traffic_filter diff --git a/macros/remote_desktop_process_running_on_system_filter.yml b/macros/remote_desktop_process_running_on_system_filter.yml deleted file mode 100644 index 854b116d87..0000000000 --- a/macros/remote_desktop_process_running_on_system_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for remote desktop process running on system -name: remote_desktop_process_running_on_system_filter diff --git a/macros/s3_accesslogs.yml b/macros/s3_accesslogs.yml new file mode 100644 index 0000000000..5bf66bb4e5 --- /dev/null +++ b/macros/s3_accesslogs.yml @@ -0,0 +1,3 @@ +definition: sourcetype=aws:s3:accesslogs +description: customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch vpc logs. Replace the macro definition with configurations for your Splunk Environmnent. +name: s3_accesslogs diff --git a/macros/schtasks_scheduling_job_on_remote_system_filter.yml b/macros/schtasks_scheduling_job_on_remote_system_filter.yml deleted file mode 100644 index fdcbfa2eec..0000000000 --- a/macros/schtasks_scheduling_job_on_remote_system_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for schtasks scheduling job on remote system -name: schtasks_scheduling_job_on_remote_system_filter diff --git a/macros/smb_traffic_spike_filter.yml b/macros/smb_traffic_spike_filter.yml deleted file mode 100644 index 08d2f3a407..0000000000 --- a/macros/smb_traffic_spike_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for SMB traffic spike detection -name: smb_traffic_spike_filter diff --git a/macros/smb_traffic_spike_mltk_filter.yml b/macros/smb_traffic_spike_mltk_filter.yml deleted file mode 100644 index ee09da9bcd..0000000000 --- a/macros/smb_traffic_spike_mltk_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for SMB traffic spike detection using MLKT -name: smb_traffic_spike_mltk_filter diff --git a/macros/stream_http.yml b/macros/stream_http.yml new file mode 100644 index 0000000000..3f01a15760 --- /dev/null +++ b/macros/stream_http.yml @@ -0,0 +1,4 @@ +definition: sourcetype=stream:http +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: stream_http diff --git a/macros/suspicious_email_attachments_filter.yml b/macros/suspicious_email_attachments_filter.yml deleted file mode 100644 index f26cb9022e..0000000000 --- a/macros/suspicious_email_attachments_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for suspicious email attachments -name: suspicious_email_attachments_filter diff --git a/macros/suspicious_reg_process_filter.yml b/macros/suspicious_reg_process_filter.yml deleted file mode 100644 index c4007321e2..0000000000 --- a/macros/suspicious_reg_process_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: suspicious_reg_process_filter \ No newline at end of file diff --git a/macros/sysmon.yml b/macros/sysmon.yml index abced6b0c7..74c7640aed 100644 --- a/macros/sysmon.yml +++ b/macros/sysmon.yml @@ -1,3 +1,4 @@ -definition: sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" -description: customer specific splunk configurations(eg- index, source, sourcetype) for Windows Sysmon Logs. Replace the macro definition with configurations for your Splunk Environmnent. +definition: sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. name: sysmon diff --git a/macros/unauthorized_dns_servers_filter.yml b/macros/unauthorized_dns_servers_filter.yml deleted file mode 100644 index 919e3fd05e..0000000000 --- a/macros/unauthorized_dns_servers_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filters for DNS Query Requests Resolved by Unauthorized DNS Servers detection -name: unauthorized_dns_servers_filter diff --git a/macros/uncommon_processes_filter.yml b/macros/uncommon_processes_filter.yml deleted file mode 100644 index 5a2cd7d62e..0000000000 --- a/macros/uncommon_processes_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: uncommon_processes_filter \ No newline at end of file diff --git a/macros/unload_sysmon_filter_driver_filter.yml b/macros/unload_sysmon_filter_driver_filter.yml deleted file mode 100644 index a77c0dac98..0000000000 --- a/macros/unload_sysmon_filter_driver_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for Unload sysmon filter driver search to filter out the false positives -name: unload_sysmon_filter_driver_filter diff --git a/macros/unsigned_image_loaded_by_LSASS_filter.yml b/macros/unsigned_image_loaded_by_LSASS_filter.yml deleted file mode 100644 index 9a7d757853..0000000000 --- a/macros/unsigned_image_loaded_by_LSASS_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Use this macro to add additional filter for unsigned image loaded by LSASS -name: unsigned_image_loaded_by_LSASS_filter diff --git a/macros/unusually_long_commandlines_filter.yml b/macros/unusually_long_commandlines_filter.yml deleted file mode 100644 index 4d3b22ce29..0000000000 --- a/macros/unusually_long_commandlines_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: unusually_long_commandlines_filter \ No newline at end of file diff --git a/macros/wineventlog_security.yml b/macros/wineventlog_security.yml index f39392ffce..00d2e3d6b8 100644 --- a/macros/wineventlog_security.yml +++ b/macros/wineventlog_security.yml @@ -1,3 +1,4 @@ -definition: eventtype="wineventlog_security" -description: Customer specific splunk configurations(eg- index, source, sourcetype) for Windows Event Logs. Replace the macro definition with configurations for your Splunk Environmnent. +definition: eventtype=wineventlog_security +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. name: wineventlog_security diff --git a/macros/wineventlog_system.yml b/macros/wineventlog_system.yml new file mode 100644 index 0000000000..d6c9a4b88b --- /dev/null +++ b/macros/wineventlog_system.yml @@ -0,0 +1,4 @@ +definition: eventtype=wineventlog_system +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: wineventlog_system diff --git a/macros/wmi.yml b/macros/wmi.yml new file mode 100644 index 0000000000..9769d5c79d --- /dev/null +++ b/macros/wmi.yml @@ -0,0 +1,4 @@ +definition: sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" +description: customer specific splunk configurations(eg- index, source, sourcetype). + Replace the macro definition with configurations for your Splunk Environmnent. +name: wmi diff --git a/macros/wmi_process_launch_filter.yml b/macros/wmi_process_launch_filter.yml deleted file mode 100644 index eb93b54cb5..0000000000 --- a/macros/wmi_process_launch_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: wmi_process_launch_filter \ No newline at end of file diff --git a/macros/wmi_script_execution_filter.yml b/macros/wmi_script_execution_filter.yml deleted file mode 100644 index 08ad0f61b4..0000000000 --- a/macros/wmi_script_execution_filter.yml +++ /dev/null @@ -1,3 +0,0 @@ -definition: search * -description: Update this macro to limit the output results to filter out false positives. -name: wmi_script_execution_filter \ No newline at end of file diff --git a/package/README/alert_actions.conf.spec b/package/README/alert_actions.conf.spec deleted file mode 100644 index 5e1a7184f3..0000000000 --- a/package/README/alert_actions.conf.spec +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (C) 2009-2016 Splunk Inc. All Rights Reserved. -# -# This file contains additional options for an alert_actions.conf file. -# -# To learn more about configuration files (including precedence) please see the documentation -# located at http://www.splunk.com/base/Documentation/latest/Admin/Aboutconfigurationfiles -# -[escu] - -enabled = [true|false|0|1] - * Whether or not this use-case is enabled. - * This exists so that we are a true noop for scheduled searches. - * action.usecase=0 action.usecase.enabled=1 - * Required. - * Defaults to false. - -version = [string] - * Version of this search - -asset_at_risk = [string] - * The type of asset that is at risk from the behavior this search is attempting to find - * Defaults to None - -category = [string] - * A description of the category that this use-case falls into - * Defaults to None - -channel = [string] - * The name of the channel the search belongs to - -confidence = [low|medium|high] - * A description of the confidence value - * Valid values are: low, medium, high - * Defaults to None - -creation_time = [datetime] - * The date & time that the search was first created - * The date-time should be formatted an epoch time (in GMT) - -datamodels = [json] - * A JSON list of the data models used by this search - * Defaults to None - -eli5 = [string] - * Text explaining this search to a 5 year old - * Defaults to None - -full_search_name = [string] - * The entire search name - * Defaults to None - -how_to_implement = [string] - * Text discussing what needs to be done to implement this search and any local modifications that can be performed - * Defaults to None - -known_false_positives = [string] - * A description of cases in which this use-case may generate false positive alerts - * Defaults to None - -mappings = [json] - * A JSON list of the kill chain phases this search covers - * Defaults to None - -modification_time = [datetime] - * The date that the search was last modified - * The date-time should be formatted an epoch time (in GMT) - -remediation = [string] - * A high-level description of how the issue described by this use-case can be remediated. - * Defaults to None - -providing_technologies = [json] - * A JSON list of the technology examples that can be used to gather data to power this search - * Defaults to None - -analytic_story = [json] - * A JSON list of the use cases this search applies to - * Defaults to None - -earliest_time_offset = [integer] - * Time in seconds before event time that the search should cover - -latest_time_offset = [integer] - * Time in seconds after event time that the search should cover - diff --git a/package/README/analytic_story.conf.spec b/package/README/analytic_story.conf.spec deleted file mode 100644 index 4aebc9f729..0000000000 --- a/package/README/analytic_story.conf.spec +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (C) 2009-2016 Splunk Inc. All Rights Reserved. -# -# This file contains all possible options for a usecases.conf file. Use this file to define a use-case. -# -# To learn more about configuration files (including precedence) please see the documentation -# located at http://www.splunk.com/base/Documentation/latest/Admin/Aboutconfigurationfiles -# -[] -category = [string] - * The category of the analytic story - * Defaults to None - -creation_time = [datetime] - * The date & time that the analytic story was first created - * The date-time should be formatted an epoch time (in GMT) - -data_models = [json] - * A JSON list of the data models used by the analytic story - * Defaults to None - -description = [string] - * A bried description of the analytic story - * Defaults to None - -id = [string] - * A description of the analytic story - * Defaults to None - -mappings = [json] - * A JSON dictionary of the different mappings this story maps to - * See appendix B for the format of this field - * Defaults to None - -modification_time = [datetime] - * The date & time that the analytic story was last modified - * The date-time should be formatted an epoch time (in GMT) - -narrative = [string] - * A longer narrative of the analytic story that describes the detection searches, any support searches, - * and the corresponding contextual and investigative searches - * Defaults to None - -references = [json] - * A JSON list of references for this story - * Defaults to None - -detection_searches = [json] - * A JSON list of the detection searches that the analytic story applies to. - * See appendix A for the format of this field - * Defaults to None - -investigative_searches = [json] - * A JSON list of the investigative searches that the analytic story applies to. - * See appendix A for the format of this field - * Defaults to None - -contextual_searches = [json] - * A JSON list of contextual searches that the analytic story applies to. - * See appendix A for the format of this field - * Defaults to None - -support_searches = [json] - * JSON list of support searches that the analytic story applies to. - * See appendix A for the format of this field - * Defaults to None - -providing_technologies = [json] - * A JSON list of example technologies that can be used to capture the data needed for the analytic story - * Defaults to None - -version = [int] - * An integer indicating which revision of the analytic story this is - * This value should start with one and increase for each release - -###### Appendix A: *_searches Specification ####### -# This can just be a list of saved search names. However, this also supports a hierarchical structure to denote searches that rely on other searches. -# -# A non-hierarchical version would look like this: -# [ "search1", "search2" ] -# -# A hierarchical version would look like this: -#[ -# "search1": [ "search1a", "search1b" ], -# "search2": [ "search2a" ] -#] - - -###### Appendix B: Mappings Specification ####### -# -# This is a dictionary of the different mappings this analytic story maps to. -# The mapping will be the key, and the value will be an array of the labels it applies to -# -# Example: -# { -# "kill_chain_phase": ["Delivery", "Command and Control"], -# "sans cis": ["CIS 9", "CIS 12"], -# "att&ck": ["Command and Control"] -# } -# - diff --git a/package/README/savedsearches.conf.example b/package/README/savedsearches.conf.example deleted file mode 100644 index 9aefc86a96..0000000000 --- a/package/README/savedsearches.conf.example +++ /dev/null @@ -1,99 +0,0 @@ -### ESCU DETECTIONS ### - -[ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] -action.escu = 0 -action.escu.enabled = 1 -description = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ -\ - This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -action.escu.creation_date = 2018-03-16 -action.escu.modification_date = 2018-03-16 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule -action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen City -action.notable = 1 -action.notable.param.nes_fields = src_ip, city -action.notable.param.rule_description = Your AWS infrastructure was provisioned from a city, $city$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $city$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 86400 -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode - - -### ESCU INVESTIGATIONS ### - -[ESCU - AWS Investigate User Activities By ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = investigative -action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN -description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. -action.escu.creation_date = 2018-01-22 -action.escu.modification_date = 2019-04-30 -action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Command and Control", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Unusual AWS EC2 Modifications"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -disabled=true -schedule_window = auto -is_visible = false -search = | search sourcetype=aws:cloudtrail userIdentity.arn={user} | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType - - - -[ESCU - Add Prohibited Processes to Enterprise Security] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security -description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. -action.escu.creation_date = 2017-06-27 -action.escu.modification_date = 2017-09-15 -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Monitor for Unauthorized Software", "SamSam Ransomware"] -action.escu.data_models = [] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. -action.escu.how_to_implement = This search should be run on each new install of ESCU. -action.escu.known_false_positives = -disabled=true -schedule_window = auto -is_visible = false -search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count diff --git a/package/README/savedsearches.conf.spec b/package/README/savedsearches.conf.spec deleted file mode 100644 index 6d5d238354..0000000000 --- a/package/README/savedsearches.conf.spec +++ /dev/null @@ -1,18 +0,0 @@ - -[] -* Create a unique stanza name for each saved search that belongs to an analytic story -* Follow the stanza name with any number of the following settings. -* If you do not specify a setting, Splunk software uses the default. - -action.escu.full_search_name = - * Full name of the search - * required - -action.escu.mappings = [json] - * Framework mappings like CIS, Kill Chain, NIST, ATTACK - -action.escu.analytic_story = - * List of analytic story the search belongs to - -action.escu.search_type = [detection | investigative | support] - * The type of this search \ No newline at end of file diff --git a/package/appserver/static/analytic_story_details.js b/package/appserver/static/analytic_story_details.js index 5426794976..932b4426f0 100644 --- a/package/appserver/static/analytic_story_details.js +++ b/package/appserver/static/analytic_story_details.js @@ -83,13 +83,6 @@ require([
-
-
- Technologies: -
-
-
-
References: @@ -140,7 +133,7 @@ require([ earliest_time: "-1h@h", latest_time: "now", cache: false, - search: "| rest /services/configs/conf-analytic_stories splunk_server=local count=0 | search title=\"" + asName + "\" | spath input=providing_technologies path={} output=tex | spath input=reference path={} output=ref | spath input=data_models path={} output=dm | table title, category, description, version, mappings, creation_date, modification_date, dm, narrative, tex, ref" + search: "| rest /services/configs/conf-analytic_stories splunk_server=local count=0 | search title=\"" + asName + "\" | spath input=reference path={} output=ref | spath input=data_models path={} output=dm | table title, category, description, version, mappings, creation_date, modification_date, dm, narrative, ref" }); $('.run-story').unbind('click'); @@ -169,7 +162,7 @@ require([ earliest_time: "-1h@h", latest_time: "now", cache: false, - search: "| rest /services/saved/searches splunk_server=local count=0 | spath input=action.escu.analytic_story path={} output=uc | search uc = \"" + asName + "\" | spath input=action.escu.data_models path={} output=dm | spath input=action.escu.providing_technologies path={} output=tex | table action.escu.full_search_name, search, description, action.escu.search_type, action.escu.how_to_implement, action.escu.eli5, action.escu.version, action.escu.mappings, dm, tex, action.escu.asset_at_risk, action.escu.confidence, action.escu.known_false_positives, updated, action.escu.modification_date, action.escu.creation_date " + search: "| rest /services/saved/searches splunk_server=local count=0 | spath input=action.escu.analytic_story path={} output=uc | search uc = \"" + asName + "\" | spath input=action.escu.data_models path={} output=dm | table action.escu.full_search_name, search, description, action.escu.search_type, action.escu.how_to_implement, action.escu.eli5, action.escu.version, action.escu.mappings, dm, tex, action.escu.asset_at_risk, action.escu.confidence, action.escu.known_false_positives, updated, action.escu.modification_date, action.escu.creation_date " }); var searchesSearch = splunkjs.mvc.Components.getInstance("s" + epoch); @@ -207,7 +200,6 @@ require([ $('#description').html(converter.makeHtml(asAttributes.description)); $('#mitre_attack').html(getValueLabels(mappings.mitre_attack, 'attack_tag')); $('#data_model').html(getValueLabels(asAttributes.dm, 'data_model_tag')); - $('#technology').html(getValueLabels(asAttributes.tex)); $('#kill_chain_phases').html(getValueLabels(mappings.kill_chain_phases, 'kill_chain_tag')); $('#cis_20').html(getValueLabels(mappings.cis20)); $('#references').html(getReferenceURLS(asAttributes.ref)); @@ -294,14 +286,6 @@ require([ ${ getValueLabels(search['dm'], 'data_model_tag') }
-
-
- Technologies -
-
- ${ getValueLabels(search['tex']) } -
-
`; diff --git a/package/appserver/static/escu_summary.js b/package/appserver/static/escu_summary.js index 7b3427b74c..572eef9abb 100644 --- a/package/appserver/static/escu_summary.js +++ b/package/appserver/static/escu_summary.js @@ -64,7 +64,6 @@ require([ tokenModel.set('form.as_cis', '*'); tokenModel.set('form.as_category', '*'); tokenModel.set('form.as_kill_chain_phase', '*'); - tokenModel.set('form.as_mitre_attack', '*'); tokenModel.set('form.as_data_models', '*'); }); @@ -72,7 +71,6 @@ require([ tokenModel.set('form.cis', '*'); tokenModel.set('form.searchtype', '*'); tokenModel.set('form.kill_chain_phase', '*'); - tokenModel.set('form.mitre_attack', '*'); tokenModel.set('form.data_models', '*'); }); }); diff --git a/package/default/analytic_stories.conf b/package/default/analytic_stories.conf index adb4b7b23d..c53d061c85 100644 --- a/package/default/analytic_stories.conf +++ b/package/default/analytic_stories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-05-25T14:45:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -12,14 +12,14 @@ category = Cloud Security creation_date = 2018-06-04 modification_date = 2018-06-04 id = 2f2f610a-d64d-48c2-b57c-967a2b49ab5a -version = 1.0 +version = 1 reference = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] detection_searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule"] -mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "mitre_technique_id": [], "nist": ["DE.AE", "PR.AC", "PR.DS"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History"] +mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId"] support_searches = ["ESCU - Previously Seen AWS Cross Account Activity"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ @@ -30,14 +30,14 @@ category = Cloud Security creation_date = 2018-03-08 modification_date = 2018-03-08 id = ced74200-8465-4bc3-bd2c-9a782eec6750 -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Execution"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name"] -support_searches = ["ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 Launches By User"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +support_searches = ["ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 AMIs"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -46,17 +46,17 @@ This Analytic Story is focused on detecting suspicious new instances in your EC2 [AWS Network ACL Activity] category = Cloud Security -creation_date = 2018-01-10 +creation_date = 2018-05-21 modification_date = 2018-05-21 id = 2e8948a5-5239-406b-b56b-6c50ff268af4 -version = 2.0 +version = 2 reference = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] detection_searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] -mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Persistence"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk"] -providing_technologies = ["AWS", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC"]} +investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS", "ESCU - Baseline of Network ACL Activity by ARN"] +data_models = [] +providing_technologies = none description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. @@ -65,14 +65,14 @@ category = Cloud Security creation_date = 2018-03-16 modification_date = 2018-03-16 id = 3338b567-3804-4261-9889-cf0ca4753c7f -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule"] -mappings = {"cis20": ["CIS 1"], "kill_chain_phases": [], "mitre_attack": [], "mitre_technique_id": [], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region"] +mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +investigative_searches = ["ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City"] support_searches = ["ESCU - Previously Seen AWS Provisioning Activity Sources"] data_models = [] -providing_technologies = ["AWS"] +providing_technologies = none description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. narrative = Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. @@ -82,14 +82,14 @@ category = Cloud Security creation_date = 2018-03-12 modification_date = 2018-03-12 id = 2e8948a5-5239-406b-b56b-6c50f1269af3 -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] detection_searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "mitre_technique_id": [], "nist": ["DE.CM", "DE.DP", "ID.AM", "PR.AC"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate AWS User Activities by user field"] -support_searches = ["ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Create a list of approved AWS service accounts", "ESCU - Previously seen API call per user roles in CloudTrail"] +mappings = {"cis20": ["CIS 1", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "DE.DP", "ID.AM", "PR.AC"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field"] +support_searches = ["ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Previously seen API call per user roles in CloudTrail", "ESCU - Create a list of approved AWS service accounts"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ @@ -98,33 +98,33 @@ The detection searches in this Analytic Story are designed to help you uncover A [Account Monitoring and Controls] category = Best Practices -creation_date = 2017-08-05 +creation_date = 2017-09-06 modification_date = 2017-09-06 id = 8892a655-6205-55f7-abba-06460e38c8ae -version = 1.0 -reference = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] +version = 1 +reference = [] detection_searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule"] -mappings = {"cis20": ["CIS 16"], "kill_chain_phases": [], "mitre_attack": ["Create Account", "Initial Access", "Persistence", "Valid Accounts"], "mitre_technique_id": [], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1136"], "nist": ["PR.IP"]} +investigative_searches = ["ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Change", "Identity_Management", "Risk"] -providing_technologies = ["Active Directory", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "macOS"] +data_models = ["Change"] +providing_technologies = none description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. [Apache Struts Vulnerability] category = Vulnerability -creation_date = 2017-03-14 +creation_date = 2018-12-06 modification_date = 2018-12-06 id = 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e -version = 1.0 +version = 1 reference = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] detection_searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 18", "CIS 3", "CIS 4", "CIS 7"], "kill_chain_phases": ["Actions on Objectives", "Delivery", "Exploitation"], "mitre_attack": ["Defense Evasion", "Discovery", "Execution", "Exploitation of Vulnerability", "System Information Discovery"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "ID.RA", "PR.IP", "PR.MA", "PR.PT", "RS.MI"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src"] +mappings = {"cis20": ["CIS 12", "CIS 18", "CIS 3", "CIS 4"], "kill_chain_phases": ["Actions on Objectives", "Delivery", "Exploitation"], "mitre_attack": ["T1082"], "nist": ["DE.AE", "DE.CM", "ID.RA", "PR.IP", "PR.MA", "PR.PT", "RS.MI"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Apache", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ @@ -143,33 +143,33 @@ It can also be very helpful to examine various behaviors of the process of inter [Asset Tracking] category = Best Practices -creation_date = 2017-06-01 +creation_date = 2017-09-13 modification_date = 2017-09-13 id = 91c676cf-0b23-438d-abee-f6335e1fce77 -version = 1.0 +version = 1 reference = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] detection_searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule"] -mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Actions on Objectives", "Delivery", "Reconnaissance"], "mitre_attack": ["Defense Evasion"], "mitre_technique_id": [], "nist": ["ID.AM", "PR.DS"]} -investigative_searches = ["ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] support_searches = ["ESCU - Count of assets by category"] -data_models = ["Identity_Management", "Network_Sessions"] -providing_technologies = ["Bro", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream"] +data_models = ["Network_Sessions"] +providing_technologies = none description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. [Brand Monitoring] category = Abuse -creation_date = 2017-06-01 +creation_date = 2017-12-19 modification_date = 2017-12-19 id = 91c676cf-0b23-438d-abee-f6335e1fce78 -version = 1.0 +version = 1 reference = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] detection_searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule"] -mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives", "Delivery"], "mitre_attack": [], "mitre_technique_id": [], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives", "Delivery"], "nist": ["PR.IP"]} +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - DNSTwist Domain Names"] -data_models = ["Authentication", "Email", "Endpoint", "Network_Resolution", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Exchange", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Email", "Network_Resolution", "Web"] +providing_technologies = none description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ @@ -180,14 +180,14 @@ category = Cloud Security creation_date = 2019-10-02 modification_date = 2019-10-02 id = 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Execution"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region"] -support_searches = ["ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Regions"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable History", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +support_searches = ["ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Regions", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Instance Types"] data_models = ["Cloud_Infrastructure"] -providing_technologies = ["AWS", "Azure", "GCP", "Splunk Enterprise Security"] +providing_technologies = none description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -196,17 +196,17 @@ This Analytic Story is focused on detecting suspicious new instances in your clo [ColdRoot MacOS RAT] category = Malware -creation_date = 2019-01-29 +creation_date = 2019-01-09 modification_date = 2019-01-09 id = bd91a2bc-d20b-4f44-a982-1bea98e86390 -version = 1.0 +version = 1 reference = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] detection_searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule"] -mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control", "Installation"], "mitre_attack": ["Collection", "Command and Control", "Execution", "Persistence"], "mitre_technique_id": [], "nist": ["DE.CM", "DE.DP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP", "PR.PT"]} +investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Alerts", "Authentication", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Linux", "Microsoft Windows", "Nessus", "OSquery", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "macOS"] +data_models = [] +providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ @@ -214,17 +214,17 @@ Searches in this Analytic Story leverage the capabilities of OSquery to address [Collection and Staging] category = Adversary Tactics -creation_date = 2018-01-08 +creation_date = 2020-02-03 modification_date = 2020-02-03 id = 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a -version = 1.1 +version = 1 reference = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] detection_searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule"] -mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Commonly Used Port", "Data Staged", "Email Collection"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043", "T1074", "T1114"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk"] -providing_technologies = ["Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint", "Network_Traffic"] +providing_technologies = none description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ @@ -235,14 +235,14 @@ category = Adversary Tactics creation_date = 2018-06-01 modification_date = 2018-06-01 id = 943773c6-c4de-4f38-89a8-0b92f98804d8 -version = 1.0 +version = 1 reference = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Defense Evasion", "Exfiltration", "Exfiltration Over Alternative Protocol", "Standard Non-Application Layer Protocol"], "mitre_technique_id": ["T1071"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk"] -providing_technologies = ["AWS", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +mappings = {"cis20": ["CIS 1", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048", "T1095"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS", "ESCU - Baseline of DNS Query Length - MLTK"] +data_models = ["Network_Resolution", "Network_Traffic"] +providing_technologies = none description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. @@ -252,47 +252,47 @@ category = Adversary Tactics creation_date = 2019-04-29 modification_date = 2019-04-29 id = 9a64ab44-9214-4639-8163-7eaa2621bd61 -version = 1.0 +version = 1 reference = ["https://github.com/kgretzky/evilginx2", "https://attack.mitre.org/techniques/T1192/", "https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/"] detection_searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule"] -mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Delivery"], "mitre_attack": ["Command and Control", "Spearphishing Link"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP"]} +mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1192"], "nist": ["DE.CM"]} investigative_searches = ["ESCU - Get Certificate logs for a domain"] support_searches = [] -data_models = ["Certificates", "Network_Resolution", "Web"] -providing_technologies = ["Bro", "Censys", "Splunk Stream"] +data_models = ["Network_Resolution"] +providing_technologies = none description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. narrative = As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2. -[Container Implantation Monitoring & Investigation] +[Container Implantation Monitoring and Investigation] category = Cloud Security creation_date = 2020-02-20 -modification_date = +modification_date = 2020-02-20 id = aa0e28b1-0521-4b6f-9d2a-7b87e34af246 -version = 1.0 +version = 1 reference = ["https://github.com/splunk/cloud-datamodel-security-research"] detection_searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule"] -mappings = {"cis20": [], "kill_chain_phases": [], "mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"], "nist": []} +mappings = {} investigative_searches = ["ESCU - Investigate AWS ECR container listing activity"] support_searches = [] -data_models = ["Cloud_Infrastructure"] -providing_technologies = ["AWS", "GCP"] +data_models = [] +providing_technologies = none description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. narrative = Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. [Credential Dumping] category = Adversary Tactics -creation_date = 2018-08-08 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = 854d78bf-d0e2-4f4e-b05c-640905f86d7a -version = 3.0 +version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] -mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["Credential Access", "Credential Dumping", "Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1003", "T1064", "T1086"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User"] +detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] +mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1003", "T1064", "T1086"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} +investigative_searches = ["ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts"] support_searches = [] -data_models = ["Authentication", "Endpoint"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ @@ -300,17 +300,17 @@ The detection searches in this Analytic Story monitor access to the Local Securi [DHS Report TA18-074A] category = Malware -creation_date = 2018-03-19 +creation_date = 2020-01-22 modification_date = 2020-01-22 id = 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef -version = 2.0 +version = 2 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] -detection_searches = ["ESCU - Create local admin accounts using net.exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg.exe Process - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 2", "CIS 3", "CIS 5", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["AppInit DLLs", "Authentication Package", "Command and Control", "Command-Line Interface", "Commonly Used Port", "Credential Access", "Defense Evasion", "Disabling Security Tools", "Execution", "Lateral Movement", "Modify Existing Service", "Modify Registry", "New Service", "Persistence", "PowerShell", "Privilege Escalation", "Registry Run Keys / Start Folder", "Scheduled Task", "Scripting", "Valid Accounts"], "mitre_technique_id": ["T1059", "T1110", "T1117", "T1135", "T1202", "T1210"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Registry Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule"] +mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 2", "CIS 3", "CIS 5", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1043", "T1050", "T1053", "T1059", "T1064", "T1078", "T1086", "T1089", "T1103", "T1112", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint", "Network_Traffic"] +providing_technologies = none description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ @@ -319,34 +319,34 @@ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to mo [DNS Amplification Attacks] category = Abuse -creation_date = 2016-08-24 +creation_date = 2016-09-13 modification_date = 2016-09-13 id = e8afd39e-3294-11e6-b39d-a45e60c6700 -version = 1.0 +version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] detection_searches = ["ESCU - Large Volume of DNS ANY Queries - Rule"] -mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "mitre_technique_id": [], "nist": ["DE.AE", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User"] +mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] -data_models = ["Network_Resolution", "Risk"] -providing_technologies = ["Bro", "Splunk Enterprise Security", "Splunk Stream"] +data_models = ["Network_Resolution"] +providing_technologies = none description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. [DNS Hijacking] category = Adversary Tactics -creation_date = 2017-11-21 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = 8169f17b-ef68-4b59-aa28-586907301221 -version = 1.0 +version = 1 reference = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Defense Evasion", "Exfiltration", "Exfiltration Over Alternative Protocol"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} investigative_searches = ["ESCU - Get DNS Server History for a host"] support_searches = ["ESCU - Discover DNS records"] data_models = ["Network_Resolution"] -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ @@ -360,65 +360,65 @@ The searches in this Analytic Story help you detect and investigate activities t [Data Protection] category = Abuse -creation_date = 2017-06-01 +creation_date = 2017-09-14 modification_date = 2017-09-14 id = 91c676cf-0b23-438d-abee-f6335e1fce33 -version = 1.0 +version = 1 reference = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] detection_searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Defense Evasion", "Exfiltration"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "DE.CM", "PR.DS", "PR.PT"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Change_Analysis", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk"] -providing_technologies = ["Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Change_Analysis", "Network_Resolution"] +providing_technologies = none description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. [Disabling Security Tools] category = Adversary Tactics -creation_date = 2018-04-09 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = fcc27099-46a0-46b0-a271-5c7dab56b6f1 -version = 2.0 +version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] -detection_searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["Command-Line Interface", "Defense Evasion", "Disabling Security Tools", "Execution", "Modify Existing Service", "Modify Registry", "New Service", "Persistence", "Privilege Escalation"], "mitre_technique_id": ["T1089"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1089", "T1112"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). [Dynamic DNS] category = Malware -creation_date = 2017-11-21 +creation_date = 2018-09-06 modification_date = 2018-09-06 id = 8169f17b-ef68-4b59-aae8-586907301221 -version = 2.0 +version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Defense Evasion", "Exfiltration", "Exfiltration Over Command and Control Channel", "Web Service"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src_ip"] +mappings = {"cis20": ["CIS 13", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1041"], "nist": ["DE.CM", "DE.DP", "PR.IP"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Network_Resolution", "Web"] +providing_technologies = none description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. -[Emotet Malware (DHS Report TA18-201A)] +[Emotet Malware DHS Report TA18-201A ] category = Malware -creation_date = 2018-09-11 +creation_date = 2020-01-27 modification_date = 2020-01-27 id = bb9f5ed2-916e-4364-bb6d-91c310efcf52 -version = 1.0 +version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] -detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 2", "CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Exploitation", "Installation"], "mitre_attack": ["Account Discovery", "AppInit DLLs", "Authentication Package", "Command and Control", "Command-Line Interface", "Commonly Used Port", "Defense Evasion", "Discovery", "Execution", "Lateral Movement", "Persistence", "Registry Run Keys / Start Folder", "Third-party Software"], "mitre_technique_id": ["T1110", "T1135", "T1193", "T1210"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] -support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security", "ESCU - Baseline of SMB Traffic - MLTK"] -data_models = ["Authentication", "Email", "Endpoint", "Network_Traffic", "Risk", "Updates", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Cuckoo", "DeepSight", "Linux", "Microsoft Exchange", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "SMTP", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "VirusTotal", "Ziften", "macOS"] +detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] +mappings = {"cis20": ["CIS 12", "CIS 2", "CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Exploitation", "Installation"], "mitre_attack": ["T1043", "T1059", "T1072", "T1087", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Add Prohibited Processes to Enterprise Security"] +data_models = ["Email", "Endpoint", "Network_Traffic"] +providing_technologies = none description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ @@ -426,17 +426,17 @@ The searches in this Analytic Story will help you find executables that are rare [Hidden Cobra Malware] category = Malware -creation_date = 2018-06-14 +creation_date = 2020-01-22 modification_date = 2020-01-22 id = baf7580b-d4b4-4774-8173-7d198e9da335 -version = 2.0 +version = 2 reference = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] -detection_searches = ["ESCU - Create or delete windows shares using net.exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Command-Line Interface", "Commonly Used Port", "Credential Access", "Execution", "Exfiltration", "Lateral Movement", "Persistence", "Remote Desktop Protocol", "Scripting"], "mitre_technique_id": ["T1059", "T1071", "T1076", "T1077", "T1110", "T1117", "T1126", "T1135", "T1202", "T1210"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host"] -support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +detection_searches = ["ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule"] +mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1059", "T1064", "T1076"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Previously seen command line arguments"] +data_models = ["Endpoint", "Network_Resolution", "Network_Traffic"] +providing_technologies = none description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ @@ -445,33 +445,33 @@ Among other searches in this Analytic Story is a detection search that looks for [Host Redirection] category = Abuse -creation_date = 2017-06-18 +creation_date = 2017-09-14 modification_date = 2017-09-14 id = 2e8948a5-5239-406b-b56b-6c50fe268af4 -version = 1.0 +version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Defense Evasion", "Exfiltration", "Exfiltration Over Alternative Protocol"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Risk"] -providing_technologies = ["Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "macOS"] +data_models = ["Network_Resolution"] +providing_technologies = none description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. [JBoss Vulnerability] category = Vulnerability -creation_date = 2016-10-04 +creation_date = 2017-09-14 modification_date = 2017-09-14 id = 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd -version = 1.0 +version = 1 reference = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] detection_searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 18", "CIS 4"], "kill_chain_phases": ["Delivery", "Reconnaissance"], "mitre_attack": ["Defense Evasion", "Discovery", "Exploitation of Vulnerability", "System Information Discovery"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "ID.RA", "PR.IP", "PR.MA", "PR.PT"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery", "Reconnaissance"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info"] support_searches = [] -data_models = ["Risk", "Vulnerabilities", "Web"] -providing_technologies = ["Apache", "Bluecoat", "Bro", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream"] +data_models = ["Web"] +providing_technologies = none description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ @@ -491,33 +491,33 @@ It can also be helpful to examine various behaviors of and the parent of the pro [Kubernetes Scanning Activity] category = Cloud Security -creation_date = 2020-03-24 +creation_date = 2020-04-15 modification_date = 2020-04-15 id = a9ef59cf-e981-4e66-9eef-bb049f695c09 -version = 1.0 +version = 1 reference = ["https://github.com/splunk/cloud-datamodel-security-research"] detection_searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule"] -mappings = {"cis20": [], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"], "nist": []} -investigative_searches = ["ESCU - Amazon EKS Kubernetes activity by src_ip", "ESCU - GCP Kubernetes activity by src_ip", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +mappings = {"kill_chain_phases": ["Reconnaissance"]} +investigative_searches = ["ESCU - GCP Kubernetes activity by src ip", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] support_searches = [] -data_models = ["Risk"] -providing_technologies = ["AWS", "GCP", "Splunk Enterprise Security"] +data_models = [] +providing_technologies = none description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. [Lateral Movement] category = Adversary Tactics -creation_date = 2016-09-13 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = 399d65dc-1f08-499b-a259-aad9051f38ad -version = 2.0 -reference = ["https://blog.binarydefense.com/reliably-detecting-pass-the-hash-through-event-log-analysis", "https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] +version = 2 +reference = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] detection_searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule"] -mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Lateral Movement", "Pass the Hash", "Persistence", "Privilege Escalation", "Remote Desktop Protocol", "Scheduled Task"], "mitre_technique_id": ["T1053", "T1075", "T1076"], "nist": ["DE.AE", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Successful Remote Desktop Authentications"] +mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053", "T1075", "T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +investigative_searches = ["ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint", "Network_Traffic"] +providing_technologies = none description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ @@ -527,17 +527,17 @@ If there is evidence of lateral movement, it is imperative for analysts to colle [Malicious PowerShell] category = Adversary Tactics -creation_date = 2016-09-18 +creation_date = 2017-08-23 modification_date = 2017-08-23 id = 2c8ff66e-0b57-42af-8ad7-912438a403fc -version = 4.0 +version = 4 reference = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1064", "T1086"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1064", "T1086"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ @@ -552,50 +552,50 @@ In the event a system is suspected of having been compromised via a malicious we [Monitor Backup Solution] category = Best Practices -creation_date = 2017-06-15 +creation_date = 2017-09-12 modification_date = 2017-09-12 id = abe807c7-1eb6-4304-ac32-6e7aacdb891d -version = 1.0 +version = 1 reference = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] detection_searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule"] -mappings = {"cis20": ["CIS 10"], "kill_chain_phases": [], "mitre_attack": [], "mitre_technique_id": [], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User"] +mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host"] support_searches = ["ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"] -data_models = ["Risk"] -providing_technologies = ["Netbackup", "Splunk Enterprise Security"] +data_models = [] +providing_technologies = none description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. narrative = Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. [Monitor for Unauthorized Software] category = Best Practices -creation_date = 2017-06-26 +creation_date = 2017-09-15 modification_date = 2017-09-15 id = 8892a655-6205-43f7-abba-06460e38c8ae -version = 1.0 +version = 1 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Prohibited Software On Endpoint - Rule"] -mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["Execution"], "mitre_technique_id": [], "nist": ["ID.AM", "PR.DS"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] -data_models = ["Authentication", "Endpoint", "Risk", "Updates", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. [Monitor for Updates] category = Best Practices -creation_date = 2017-08-15 +creation_date = 2017-09-15 modification_date = 2017-09-15 id = 9ef8d677-7b52-4213-a038-99cfc7acc2d8 -version = 1.0 +version = 1 reference = ["https://learn.cisecurity.org/20-controls-download"] detection_searches = ["ESCU - No Windows Updates in a time frame - Rule"] -mappings = {"cis20": ["CIS 18"], "kill_chain_phases": [], "mitre_attack": [], "mitre_technique_id": [], "nist": ["PR.MA", "PR.PT"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +mappings = {"cis20": ["CIS 18"], "nist": ["PR.MA"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] -data_models = ["Risk", "Updates"] -providing_technologies = ["Microsoft Windows", "Splunk Enterprise Security"] +data_models = ["Updates"] +providing_technologies = none description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ @@ -603,34 +603,34 @@ Microsoft releases updates for Windows systems on a monthly cadence. They should [Netsh Abuse] category = Abuse -creation_date = 2017-01-04 +creation_date = 2017-01-05 modification_date = 2017-01-05 id = 2b1800dd-92f9-47ec-a981-fdf1351e5f65 -version = 1.0 +version = 1 reference = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] detection_searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule"] -mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Command-Line Interface", "Defense Evasion", "Disabling Security Tools", "Execution", "Persistence"], "mitre_technique_id": [], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["DE.CM", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. [Orangeworm Attack Group] category = Malware -creation_date = 2018-06-14 +creation_date = 2020-01-22 modification_date = 2020-01-22 id = bb9f5ed2-916e-4364-bb6d-97c370efcf52 -version = 2.0 +version = 2 reference = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] -detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule"] -mappings = {"cis20": ["CIS 2", "CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["Command-Line Interface", "Defense Evasion", "Disabling Security Tools", "Execution", "Modify Existing Service", "New Service", "Persistence", "Privilege Escalation", "Scripting"], "mitre_technique_id": ["T1050", "T1059", "T1117", "T1202"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1064", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ @@ -643,14 +643,14 @@ category = Adversary Tactics creation_date = 2019-04-29 modification_date = 2019-04-29 id = 57226b40-94f3-4ce5-b101-a75f67759c27 -version = 1.0 +version = 1 reference = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] -detection_searches = ["ESCU - Detect Oulook.exe writing a .zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule"] -mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "mitre_technique_id": [], "nist": ["ID.AM", "PR.DS"]} +detection_searches = ["ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule"] +mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} investigative_searches = ["ESCU - Get Parent Process Info"] support_searches = [] -data_models = ["Endpoint"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +data_models = [] +providing_technologies = none description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ @@ -663,17 +663,17 @@ This Analytic Story focuses on detecting signs that a malicious payload has been [Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns] category = Adversary Tactics -creation_date = 2018-07-24 +creation_date = 2020-01-22 modification_date = 2020-01-22 id = 988C59C5-0A1C-45B6-A555-0C62276E327E -version = 1.0 +version = 1 reference = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] detection_searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["AppInit DLLs", "Authentication Package", "Command-Line Interface", "Execution", "Persistence", "PowerShell", "Registry Run Keys / Start Folder", "Scripting"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "nist": ["DE.AE", "DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1059", "T1064", "T1086", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ @@ -706,66 +706,66 @@ If behavioral searches included in this story yield positive hits, iDefense reco [Prohibited Traffic Allowed or Protocol Mismatch] category = Best Practices -creation_date = 2017-04-18 +creation_date = 2017-09-11 modification_date = 2017-09-11 id = 6d13121c-90f3-446d-8ac3-27efbbc65218 -version = 1.0 +version = 1 reference = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Defense Evasion", "Exfiltration", "Exfiltration Over Alternative Protocol"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Network_Resolution", "Network_Traffic"] +providing_technologies = none description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. [Ransomware] category = Malware -creation_date = 2017-07-24 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = cf309d0d-d4aa-4fbb-963d-1e79febd3756 -version = 1.1 +version = 1 reference = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] detection_searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule"] -mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 3", "CIS 5", "CIS 6", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["AppInit DLLs", "Authentication Package", "Command and Control", "Commonly Used Port", "Defense Evasion", "Execution", "Exfiltration", "Exfiltration Over Alternative Protocol", "Impact", "Indicator Removal on Host", "Lateral Movement", "Masquerading", "Persistence", "Registry Run Keys / Start Folder", "Scheduled Task", "Windows Management Instrumentation"], "mitre_technique_id": ["T1110", "T1135", "T1210", "T1490"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 3", "CIS 5", "CIS 6", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1036", "T1043", "T1047", "T1048", "T1053", "T1070", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk", "Updates", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Netbackup", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint", "Network_Traffic"] +providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. -[Router & Infrastructure Security] +[Router and Infrastructure Security] category = Best Practices -creation_date = 2017-06-01 +creation_date = 2017-09-12 modification_date = 2017-09-12 id = 91c676cf-0b23-438d-abee-f6335e177e77 -version = 1.0 +version = 1 reference = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] detection_searches = ["ESCU - Detect New Login Attempts to Routers - Rule"] -mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "mitre_technique_id": [], "nist": ["PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Risk"] -providing_technologies = ["Active Directory", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "macOS"] +data_models = ["Authentication"] +providing_technologies = none description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. [SQL Injection] category = Adversary Tactics -creation_date = 2016-09-13 +creation_date = 2017-09-19 modification_date = 2017-09-19 id = 4f6632f5-449c-4686-80df-57625f59bab3 -version = 1.0 +version = 1 reference = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] detection_searches = ["ESCU - SQL Injection with Long URLs - Rule"] -mappings = {"cis20": ["CIS 13", "CIS 18", "CIS 4"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Commonly Used Port", "Defense Evasion", "Execution", "Exploitation of Vulnerability"], "mitre_technique_id": [], "nist": ["DE.CM", "ID.RA", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] -data_models = ["Authentication", "Risk", "Web"] -providing_technologies = ["Bro", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "macOS"] +data_models = ["Web"] +providing_technologies = none description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. @@ -775,14 +775,14 @@ category = Malware creation_date = 2018-12-13 modification_date = 2018-12-13 id = c4b89506-fbcf-4cb7-bfd6-527e54789604 -version = 1.0 +version = 1 reference = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] detection_searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule"] -mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 16", "CIS 18", "CIS 2", "CIS 3", "CIS 4", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Installation", "Reconnaissance"], "mitre_attack": ["Command-Line Interface", "Credential Access", "Defense Evasion", "Discovery", "Execution", "Exploitation of Vulnerability", "Impact", "Lateral Movement", "Remote Desktop Protocol", "System Information Discovery"], "mitre_technique_id": ["T1076", "T1490"], "nist": ["DE.AE", "DE.CM", "ID.AM", "ID.RA", "PR.AC", "PR.DS", "PR.IP", "PR.MA", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 16", "CIS 18", "CIS 2", "CIS 3", "CIS 4", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Installation", "Reconnaissance"], "mitre_attack": ["T1059", "T1076", "T1082"], "nist": ["DE.AE", "DE.CM", "ID.AM", "ID.RA", "PR.AC", "PR.DS", "PR.IP", "PR.MA", "PR.PT"]} +investigative_searches = ["ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk", "Updates", "Vulnerabilities", "Web"] -providing_technologies = ["Apache", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Netbackup", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint", "Network_Traffic", "Web"] +providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ @@ -796,30 +796,30 @@ category = Vulnerability creation_date = 2018-01-08 modification_date = 2018-01-08 id = 6d3306f6-bb2b-4219-8609-8efad64032f2 -version = 1.0 +version = 1 reference = ["https://meltdownattack.com/"] detection_searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule"] -mappings = {"cis20": ["CIS 4"], "kill_chain_phases": [], "mitre_attack": [], "mitre_technique_id": [], "nist": ["DE.CM", "ID.RA", "PR.IP", "RS.MI"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 4"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Systems Ready for Spectre-Meltdown Windows Patch"] -data_models = ["Authentication", "Change_Analysis", "Risk", "Vulnerabilities"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Qualys", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Vulnerabilities"] +providing_technologies = none description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. [Splunk Enterprise Vulnerability] category = Vulnerability -creation_date = 2016-09-13 +creation_date = 2017-09-19 modification_date = 2017-09-19 id = 4e692b96-de2d-4bd1-9105-37e2368a8db1 -version = 1.0 +version = 1 reference = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] detection_searches = ["ESCU - Open Redirect in Splunk Web - Rule"] -mappings = {"cis20": ["CIS 18", "CIS 3", "CIS 4"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "mitre_technique_id": [], "nist": ["DE.CM", "ID.RA", "PR.AC", "PR.IP", "PR.PT", "RS.MI"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] -data_models = ["Risk"] -providing_technologies = ["Splunk Enterprise", "Splunk Enterprise Security"] +data_models = [] +providing_technologies = none description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ \ @@ -837,14 +837,14 @@ category = Vulnerability creation_date = 2018-06-14 modification_date = 2018-06-14 id = 1fc34cbc-34e9-43ba-87ab-6811c9e95400 -version = 1.0 +version = 1 reference = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] detection_searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule"] -mappings = {"cis20": ["CIS 18", "CIS 3", "CIS 4"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "mitre_technique_id": [], "nist": ["DE.CM", "ID.RA", "PR.AC", "PR.IP", "PR.PT", "RS.MI"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info"] support_searches = [] -data_models = ["Network_Traffic", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Palo Alto Firewall", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream"] +data_models = [] +providing_technologies = none description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ @@ -856,46 +856,46 @@ category = Cloud Security creation_date = 2018-02-09 modification_date = 2018-02-09 id = 2e8948a5-5239-406b-b56b-6c50f1268af3 -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Execution"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name"] -support_searches = ["ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Launches By User"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +support_searches = ["ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen EC2 Launches By User"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. [Suspicious AWS Login Activities] category = Cloud Security -creation_date = 2018-02-24 +creation_date = 2019-05-01 modification_date = 2019-05-01 id = 2e8948a5-5239-406b-b56b-6c59f1268af3 -version = 1.0 +version = 1 reference = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] detection_searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule"] -mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.DP"]} +mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP"]} investigative_searches = ["ESCU - AWS Investigate User Activities By ARN"] -support_searches = ["ESCU - Previously seen users in CloudTrail", "ESCU - Update previously seen users in CloudTrail"] +support_searches = ["ESCU - Update previously seen users in CloudTrail", "ESCU - Previously seen users in CloudTrail"] data_models = [] -providing_technologies = ["AWS"] +providing_technologies = none description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. [Suspicious AWS S3 Activities] category = Cloud Security -creation_date = 2018-06-25 +creation_date = 2018-07-24 modification_date = 2018-07-24 id = 2e8948a5-5239-406b-b56b-6c50w3168af3 -version = 2.0 +version = 2 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] detection_searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule"] -mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution", "Exfiltration", "Initial Access"], "mitre_technique_id": [], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name"] +mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName"] support_searches = ["ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ @@ -906,14 +906,14 @@ category = Cloud Security creation_date = 2018-05-07 modification_date = 2018-05-07 id = 2e8948a5-5239-406b-b56b-6c50f2168af3 -version = 1.0 +version = 1 reference = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] detection_searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] -mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.AC"]} +investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS"] -data_models = ["Authentication", "Endpoint", "Network_Traffic", "Risk"] -providing_technologies = ["AWS", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = [] +providing_technologies = none description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ @@ -922,49 +922,49 @@ The searches in this Analytic Story will monitor your AWS network traffic for ev [Suspicious Command-Line Executions] category = Adversary Tactics -creation_date = 2017-10-09 +creation_date = 2020-02-03 modification_date = 2020-02-03 id = f4368ddf-d59f-4192-84f6-778ac5a3ffc7 -version = 2.1 +version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] -detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Exploitation"], "mitre_attack": ["Command-Line Interface", "Defense Evasion", "Execution", "Masquerading", "Persistence", "Scripting"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Exploitation"], "mitre_attack": ["T1036", "T1059", "T1064"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. [Suspicious DNS Traffic] category = Adversary Tactics -creation_date = 2016-09-13 +creation_date = 2017-09-18 modification_date = 2017-09-18 id = 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56 -version = 1.0 +version = 1 reference = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule"] -mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Defense Evasion", "Exfiltration", "Exfiltration Over Alternative Protocol"], "mitre_technique_id": ["T1071"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Baseline of DNS Query Length - MLTK"] -data_models = ["Authentication", "Endpoint", "Network_Resolution", "Network_Traffic", "Risk"] -providing_technologies = ["Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Network_Resolution"] +providing_technologies = none description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. [Suspicious Emails] category = Adversary Tactics -creation_date = 2017-03-24 +creation_date = 2020-01-27 modification_date = 2020-01-27 id = 2b1800dd-92f9-47ec-a981-fdf1351e5d55 -version = 1.0 +version = 1 reference = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] -detection_searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] -mappings = {"cis20": ["CIS 12", "CIS 3", "CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Execution"], "mitre_technique_id": ["T1193"], "nist": ["DE.AE", "PR.IP"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] +mappings = {"cis20": ["CIS 12", "CIS 3", "CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - DNSTwist Domain Names"] -data_models = ["Authentication", "Email", "Risk", "UEBA", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Cuckoo", "DeepSight", "Linux", "Microsoft Exchange", "Microsoft Windows", "Palo Alto Firewall", "SMTP", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream", "VirusTotal", "macOS"] +data_models = ["Email", "UEBA"] +providing_technologies = none description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ @@ -974,17 +974,17 @@ Once a phishing message has been detected, the next steps are to answer the foll [Suspicious MSHTA Activity] category = Adversary Tactics -creation_date = 2018-08-07 +creation_date = 2020-02-03 modification_date = 2020-02-03 id = 2b1800dd-92f9-47dd-a981-fdf13w1q5d55 -version = 1.1 +version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] -detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect mshta.exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule"] -mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["AppInit DLLs", "Authentication Package", "Command-Line Interface", "Execution", "Persistence", "Registry Run Keys / Start Folder"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule"] +mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1059", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ @@ -995,14 +995,14 @@ category = Adversary Tactics creation_date = 2020-04-02 modification_date = 2020-04-02 id = 9cbd34af-8f39-4476-a423-bacd126c750b -version = 1.0 +version = 1 reference = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] detection_searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule"] -mappings = {"cis20": ["CIS 16"], "kill_chain_phases": [], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"] +mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} +investigative_searches = ["ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app"] support_searches = [] data_models = [] -providing_technologies = ["Okta"] +providing_technologies = none description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ @@ -1010,17 +1010,17 @@ With people moving quickly to adopt web-based applications and ways to manage th [Suspicious WMI Use] category = Adversary Tactics -creation_date = 2017-01-13 +creation_date = 2018-10-23 modification_date = 2018-10-23 id = c8ddc5be-69bc-4202-b3ab-4010b27d7ad5 -version = 2.0 +version = 2 reference = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] detection_searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Windows Management Instrumentation", "Windows Management Instrumentation Event Subscription"], "mitre_technique_id": [], "nist": ["PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ @@ -1031,14 +1031,14 @@ category = Adversary Tactics creation_date = 2018-05-31 modification_date = 2018-05-31 id = 2b1800dd-92f9-47dd-a981-fdf1351e5d55 -version = 1.0 +version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] -detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Accessibility Features", "AppInit DLLs", "Application Shimming", "Authentication Package", "Change Default File Association", "Defense Evasion", "Lateral Movement", "Local Port Monitor", "Modify Registry", "Persistence", "Privilege Escalation", "Registry Run Keys / Start Folder"], "mitre_technique_id": ["T1183"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015", "T1042", "T1103", "T1112", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = [] -data_models = ["Authentication", "Change_Analysis", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ @@ -1049,31 +1049,31 @@ category = Cloud Security creation_date = 2018-04-09 modification_date = 2018-04-09 id = 73de57ef-0dfc-411f-b1e7-fa24428aeae0 -version = 1.0 +version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule"] -mappings = {"cis20": ["CIS 1"], "kill_chain_phases": [], "mitre_attack": [], "mitre_technique_id": [], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] +mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId"] support_searches = ["ESCU - Previously Seen EC2 Modifications By User"] data_models = [] -providing_technologies = ["AWS", "Splunk Enterprise Security"] +providing_technologies = none description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. [Unusual Processes] category = Malware -creation_date = 2016-08-09 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = f4368e3f-d59f-4192-84f6-748ac5a3ddb6 -version = 2.1 +version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] -mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["Accessibility Features", "Defense Evasion", "Execution", "Masquerading", "Rundll32"], "mitre_technique_id": [], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1015", "T1036", "T1085"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK"] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ @@ -1081,33 +1081,33 @@ In the event an unusual process is identified, it is imperative to better unders [Use of Cleartext Protocols] category = Best Practices -creation_date = 2016-09-13 +creation_date = 2017-09-15 modification_date = 2017-09-15 id = 826e6431-aeef-41b4-9fc0-6d0985d65a21 -version = 1.0 +version = 1 reference = ["https://www.monkey.org/~dugsong/dsniff/"] detection_searches = ["ESCU - Protocols passing authentication in cleartext - Rule"] -mappings = {"cis20": ["CIS 14", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Reconnaissance"], "mitre_attack": ["Collection", "Credential Access", "Lateral Movement"], "mitre_technique_id": [], "nist": ["DE.AE", "PR.AC", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Endpoint", "Network_Traffic", "Risk"] -providing_technologies = ["Bluecoat", "Bro", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream"] +data_models = ["Network_Traffic"] +providing_technologies = none description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. [Web Fraud Detection] category = Abuse -creation_date = 2018-07-12 +creation_date = 2018-10-08 modification_date = 2018-10-08 id = 31337aaa-bc22-4752-b599-ef112dq1dq7a -version = 1.0 +version = 1 reference = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] detection_searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule"] -mappings = {"cis20": ["CIS 16", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Create Account", "Initial Access", "Persistence", "Valid Accounts"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "DE.DP"]} -investigative_searches = ["ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session_id"] +mappings = {"cis20": ["CIS 16", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1136"], "nist": ["DE.AE", "DE.CM", "DE.DP"]} +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id"] support_searches = [] -data_models = ["Email"] -providing_technologies = ["Microsoft Exchange", "Splunk Enterprise Security", "Splunk Stream"] +data_models = [] +providing_technologies = none description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ @@ -1118,17 +1118,17 @@ Another search detects incidents wherein a single password is used across multip [Windows Defense Evasion Tactics] category = Adversary Tactics -creation_date = 2017-10-11 +creation_date = 2018-05-31 modification_date = 2018-05-31 id = 56e24a28-5003-4047-b2db-e8f3c4618064 -version = 1.0 +version = 1 reference = ["https://attack.mitre.org/wiki/Defense_Evasion"] -detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg.exe Process - Rule"] -mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools", "Lateral Movement", "Modify Registry", "Persistence"], "mitre_technique_id": [], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg exe Process - Rule"] +mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089", "T1112"], "nist": ["DE.CM", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. @@ -1137,14 +1137,14 @@ category = Malware creation_date = 2018-01-26 modification_date = 2018-01-26 id = 30552a76-ac78-48e4-b3c0-de4e34e9563d -version = 1.0 +version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] detection_searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Change Default File Association", "Execution", "Persistence"], "mitre_technique_id": [], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ @@ -1154,50 +1154,50 @@ Run the searches in this story to detect and investigate suspicious behavior tha [Windows Log Manipulation] category = Adversary Tactics -creation_date = 2017-02-17 +creation_date = 2017-09-12 modification_date = 2017-09-12 id = b6db2c60-a281-48b4-95f1-2cd99ed56835 -version = 2.0 +version = 2 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] detection_searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule"] -mappings = {"cis20": ["CIS 10", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Impact", "Indicator Removal on Host"], "mitre_technique_id": ["T1490"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +mappings = {"cis20": ["CIS 10", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk", "Vulnerabilities", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). [Windows Persistence Techniques] category = Adversary Tactics -creation_date = 2017-04-19 +creation_date = 2018-05-31 modification_date = 2018-05-31 id = 30874d4f-20a1-488f-85ec-5d52ef74e3f9 -version = 2.0 +version = 2 reference = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] -detection_searches = ["ESCU - Detect Path Interception By Creation Of program.exe - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule"] -mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["AppInit DLLs", "Application Shimming", "Authentication Package", "Defense Evasion", "Disabling Security Tools", "Execution", "Lateral Movement", "Local Port Monitor", "Modify Existing Service", "New Service", "Persistence", "Privilege Escalation", "Registry Run Keys / Start Folder", "Scheduled Task"], "mitre_technique_id": [], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1053", "T1089", "T1103", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = [] -data_models = ["Authentication", "Change_Analysis", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. [Windows Privilege Escalation] category = Adversary Tactics -creation_date = 2017-12-07 +creation_date = 2020-02-04 modification_date = 2020-02-04 id = 644e22d3-598a-429c-a007-16fdb802cae5 -version = 2.0 +version = 2 reference = ["https://attack.mitre.org/tactics/TA0004/"] -detection_searches = ["ESCU - Child Processes of Spoolsv.exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule"] -mappings = {"cis20": ["CIS 2", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["Accessibility Features", "Execution", "Exploitation for Privilege Escalation", "Persistence", "Privilege Escalation"], "mitre_technique_id": ["T1183"], "nist": ["DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +detection_searches = ["ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule"] +mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1015", "T1068"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] support_searches = [] -data_models = ["Authentication", "Endpoint", "Risk", "Web"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. @@ -1206,14 +1206,14 @@ category = Malware creation_date = 2017-11-02 modification_date = 2017-11-02 id = 6dbd810e-f66d-414b-8dfc-e46de55cbfe2 -version = 3.0 +version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] -detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule"] -mappings = {"cis20": ["CIS 2", "CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools", "Execution", "Modify Existing Service", "New Service", "Persistence", "Privilege Escalation"], "mitre_technique_id": ["T1050"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] support_searches = ["ESCU - Previously Seen Running Windows Services"] -data_models = ["Authentication", "Endpoint", "Risk"] -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] +data_models = ["Endpoint"] +providing_technologies = none description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. diff --git a/package/default/data/ui/alerts/escu_contextualize.html b/package/default/data/ui/alerts/escu_contextualize.html deleted file mode 100644 index c46913dea7..0000000000 --- a/package/default/data/ui/alerts/escu_contextualize.html +++ /dev/null @@ -1,2 +0,0 @@ -
-
diff --git a/package/default/data/ui/alerts/escu_investigate.html b/package/default/data/ui/alerts/escu_investigate.html deleted file mode 100644 index c46913dea7..0000000000 --- a/package/default/data/ui/alerts/escu_investigate.html +++ /dev/null @@ -1,2 +0,0 @@ -
-
diff --git a/package/default/data/ui/panels/workbench_panel_all_backup_logs_for_host.xml b/package/default/data/ui/panels/workbench_panel_all_backup_logs_for_host.xml new file mode 100644 index 0000000000..1ff29ae929 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_all_backup_logs_for_host.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype="netbackup_logs" dest=$dest$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_amazon_eks_kubernetes_activity_by_src_ip.xml b/package/default/data/ui/panels/workbench_panel_amazon_eks_kubernetes_activity_by_src_ip.xml new file mode 100644 index 0000000000..e09827e48a --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_amazon_eks_kubernetes_activity_by_src_ip.xml @@ -0,0 +1,9 @@ + + + + sourcetype="aws:cloudwatchlogs:eks" sourceIPs{}=$src_ip$|rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_accesskeyid.xml b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_accesskeyid.xml new file mode 100644 index 0000000000..10bb4cee2d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_accesskeyid.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail userIdentity.accessKeyId=$accessKeyId$ | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_arn.xml b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_arn.xml new file mode 100644 index 0000000000..fec847550b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_arn.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail userIdentity.arn=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_source_user.xml b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_source_user.xml new file mode 100644 index 0000000000..ff7ad317cd --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_investigate_user_activities_by_source_user.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail userIdentity.arn=$src_user$ | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_network_acl_details_from_id.xml b/package/default/data/ui/panels/workbench_panel_aws_network_acl_details_from_id.xml new file mode 100644 index 0000000000..e637d48978 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_network_acl_details_from_id.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:description id=$networkAclId$ | table id account_id vpc_id network_acl_entries{}.* + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_network_interface_details_via_resourceid.xml b/package/default/data/ui/panels/workbench_panel_aws_network_interface_details_via_resourceid.xml new file mode 100644 index 0000000000..804d19a1d0 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_network_interface_details_via_resourceid.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:config resourceId=$resourceId$ | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_aws_s3_bucket_details_via_bucketname.xml b/package/default/data/ui/panels/workbench_panel_aws_s3_bucket_details_via_bucketname.xml new file mode 100644 index 0000000000..22732ef048 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_aws_s3_bucket_details_via_bucketname.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:config resourceId=$bucketName$ | table resourceCreationTime resourceId awsRegion action aws_account_id supplementaryConfiguration.AccessControlList + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_gcp_kubernetes_activity_by_src_ip.xml b/package/default/data/ui/panels/workbench_panel_gcp_kubernetes_activity_by_src_ip.xml new file mode 100644 index 0000000000..568cd26380 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_gcp_kubernetes_activity_by_src_ip.xml @@ -0,0 +1,9 @@ + + + + sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp={src_ip} | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name data.resource.type + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_city.xml b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_city.xml new file mode 100644 index 0000000000..000cfe9942 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_city.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_country.xml b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_country.xml new file mode 100644 index 0000000000..9830b3459d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_country.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Country=$Country$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_ip_address.xml b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_ip_address.xml new file mode 100644 index 0000000000..008d268420 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_ip_address.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search sourceIPAddress=$src_ip$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion, eventName, errorCode + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_region.xml b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_region.xml new file mode 100644 index 0000000000..4a7a1cdb76 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_all_aws_activity_from_region.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_authentication_logs_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_authentication_logs_for_endpoint.xml new file mode 100644 index 0000000000..46d2d923ff --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_authentication_logs_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | tstats count from datamodel=Authentication where Authentication.dest=$dest$ by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_backup_logs_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_backup_logs_for_endpoint.xml new file mode 100644 index 0000000000..afdbeba57d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_backup_logs_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_certificate_logs_for_a_domain.xml b/package/default/data/ui/panels/workbench_panel_get_certificate_logs_for_a_domain.xml new file mode 100644 index 0000000000..2b6d49308a --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_certificate_logs_for_a_domain.xml @@ -0,0 +1,9 @@ + + + + | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$ by All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | `drop_dm_object_name(All_Certificates)` | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_dns_server_history_for_a_host.xml b/package/default/data/ui/panels/workbench_panel_get_dns_server_history_for_a_host.xml new file mode 100644 index 0000000000..1039a71369 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_dns_server_history_for_a_host.xml @@ -0,0 +1,9 @@ + + + + | search tag=dns src_ip=$src_ip$ dest_port=53 | streamstats time_window=1d count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort -count + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_dns_traffic_ratio.xml b/package/default/data/ui/panels/workbench_panel_get_dns_traffic_ratio.xml new file mode 100644 index 0000000000..dc42dd8e39 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_dns_traffic_ratio.xml @@ -0,0 +1,9 @@ + + + + | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 All_Traffic.src=$src_ip$ All_Traffic.dest=$dest_ip$ | eval ratio = (bytes_out/bytes_in) | table ratio + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_ec2_instance_details_by_instanceid.xml b/package/default/data/ui/panels/workbench_panel_get_ec2_instance_details_by_instanceid.xml new file mode 100644 index 0000000000..7dbc21d376 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_ec2_instance_details_by_instanceid.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_ec2_launch_details.xml b/package/default/data/ui/panels/workbench_panel_get_ec2_launch_details.xml new file mode 100644 index 0000000000..e7f2a45294 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_ec2_launch_details.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId=$dest$ |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName | table arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_email_info.xml b/package/default/data/ui/panels/workbench_panel_get_email_info.xml new file mode 100644 index 0000000000..6afb5067bf --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_email_info.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Email.All_Email | search message_id=$message_id$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_emails_from_specific_sender.xml b/package/default/data/ui/panels/workbench_panel_get_emails_from_specific_sender.xml new file mode 100644 index 0000000000..21a977f746 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_emails_from_specific_sender.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Email.All_Email | search src_user=$src_user$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address.xml b/package/default/data/ui/panels/workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address.xml new file mode 100644 index 0000000000..e92d6d3f65 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_history_of_email_sources.xml b/package/default/data/ui/panels/workbench_panel_get_history_of_email_sources.xml new file mode 100644 index 0000000000..8d26ae3247 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_history_of_email_sources.xml @@ -0,0 +1,9 @@ + + + + |tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_endpoint.xml new file mode 100644 index 0000000000..a3068ae9c1 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_user.xml b/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_user.xml new file mode 100644 index 0000000000..3c8c81230b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_logon_rights_modifications_for_user.xml @@ -0,0 +1,9 @@ + + + + | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_notable_history.xml b/package/default/data/ui/panels/workbench_panel_get_notable_history.xml new file mode 100644 index 0000000000..f4e93f2338 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_notable_history.xml @@ -0,0 +1,9 @@ + + + + | search `notable` | search dest=$dest$ | table _time, rule_name, owner, priority, severity, status_description + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_notable_info.xml b/package/default/data/ui/panels/workbench_panel_get_notable_info.xml new file mode 100644 index 0000000000..141520df51 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_notable_info.xml @@ -0,0 +1,9 @@ + + + + | search `notable_by_id($event_id$)` | table time, rule_name, dest, dest_asset_id, dest_owner, priority, severity, owner, status_description + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_outbound_emails_to_hidden_cobra_threat_actors.xml b/package/default/data/ui/panels/workbench_panel_get_outbound_emails_to_hidden_cobra_threat_actors.xml new file mode 100644 index 0000000000..d67b828a9b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_outbound_emails_to_hidden_cobra_threat_actors.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Email.All_Email | search recipient=misswang8107@gmail.com OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time) as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_parent_process_info.xml b/package/default/data/ui/panels/workbench_panel_get_parent_process_info.xml new file mode 100644 index 0000000000..09eba16eb6 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_parent_process_info.xml @@ -0,0 +1,9 @@ + + + + | tstats `summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = $process_name$ Processes.dest = $dest$ by Processes.user Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_process_file_activity.xml b/package/default/data/ui/panels/workbench_panel_get_process_file_activity.xml new file mode 100644 index 0000000000..28f5412de6 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_process_file_activity.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, values(Filesystem.process_id) as process_id from datamodel=Endpoint.Filesystem where Filesystem.dest=$dest$ Filesystem.process_id=$process_id$ by Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)` | sort _time | table _time, process_id, dest, action, file_name, file_path + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_process_info.xml b/package/default/data/ui/panels/workbench_panel_get_process_info.xml new file mode 100644 index 0000000000..20d3333796 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_process_info.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Proceses.dest=$dest$ Proceses.process_name=$process_name$ by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_process_information_for_port_activity.xml b/package/default/data/ui/panels/workbench_panel_get_process_information_for_port_activity.xml new file mode 100644 index 0000000000..9898b46006 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_process_information_for_port_activity.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=$dest_port$ by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_process_registry_activity.xml b/package/default/data/ui/panels/workbench_panel_get_process_registry_activity.xml new file mode 100644 index 0000000000..e33c11f498 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_process_registry_activity.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` values(Registry.registry_key_name) as registry_key_name, values(Registry.dest) as dest, values(Registry.process_id) as process_id from datamodel=Endpoint.Registry where Registry.process_id=$process_id$ AND Registry.dest=$dest$ by Registry.registry_path, Registry.action, _time | `drop_dm_object_name(Registry)` | sort _time | table _time, process_id, dest, action, registry_key_name, registry_path + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_process_responsible_for_the_dns_traffic.xml b/package/default/data/ui/panels/workbench_panel_get_process_responsible_for_the_dns_traffic.xml new file mode 100644 index 0000000000..318ae20b38 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_process_responsible_for_the_dns_traffic.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_registry_activities.xml b/package/default/data/ui/panels/workbench_panel_get_registry_activities.xml new file mode 100644 index 0000000000..73bb5bee45 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_registry_activities.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path values(Registry.registry_key_name) as registry_key_name count FROM datamodel=Endpoint.Registry where Registry.dest = "$dest$" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | join [| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by Processes.process_id | `drop_dm_object_name("Processes")`] + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_endpoint.xml new file mode 100644 index 0000000000..f9e4e70b8a --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | from datamodel:Risk.All_Risk | search risk_object_type=system risk_object=$dest$ | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_user.xml b/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_user.xml new file mode 100644 index 0000000000..4dcf2fd577 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_risk_modifiers_for_user.xml @@ -0,0 +1,9 @@ + + + + | from datamodel:Risk.All_Risk | search risk_object_type=user risk_object=$user$ | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml b/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml new file mode 100644 index 0000000000..b4642ac7a7 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml @@ -0,0 +1,9 @@ + + + + sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_update_logs_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_update_logs_for_endpoint.xml new file mode 100644 index 0000000000..eb3ee6c657 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_update_logs_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Updates.Updates | search (vendor_product="Microsoft Windows" OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest=$dest$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_user_information_from_identity_table.xml b/package/default/data/ui/panels/workbench_panel_get_user_information_from_identity_table.xml new file mode 100644 index 0000000000..9269961083 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_user_information_from_identity_table.xml @@ -0,0 +1,9 @@ + + + + | `identities` | search identity=$user$ | table _time, identity, first, last, email, category, watchlist + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_vulnerability_logs_for_endpoint.xml b/package/default/data/ui/panels/workbench_panel_get_vulnerability_logs_for_endpoint.xml new file mode 100644 index 0000000000..30b224262c --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_vulnerability_logs_for_endpoint.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Vulnerabilities.Vulnerabilities | search dest=$dest$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_get_web_session_information_via_session_id.xml b/package/default/data/ui/panels/workbench_panel_get_web_session_information_via_session_id.xml new file mode 100644 index 0000000000..1be6d5c2c1 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_get_web_session_information_via_session_id.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=stream:http $session_id$ | stats values(url) values(http_user_agent) by src_ip status + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_aws_activities_via_region_name.xml b/package/default/data/ui/panels/workbench_panel_investigate_aws_activities_via_region_name.xml new file mode 100644 index 0000000000..669b2a7e63 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_aws_activities_via_region_name.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail awsRegion=$awsRegion$| rename requestParameters.instancesSet.items{}.instanceId as instanceId| stats values(eventName) by userName instanceId + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml b/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml new file mode 100644 index 0000000000..30e3630ed5 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml @@ -0,0 +1,9 @@ + + + + |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count > 1 + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_aws_user_activities_by_user_field.xml b/package/default/data/ui/panels/workbench_panel_investigate_aws_user_activities_by_user_field.xml new file mode 100644 index 0000000000..a4b65f0856 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_aws_user_activities_by_user_field.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=aws:cloudtrail user=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_cloud_compute_instance_activities.xml b/package/default/data/ui/panels/workbench_panel_investigate_cloud_compute_instance_activities.xml new file mode 100644 index 0000000000..5a042540fc --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_cloud_compute_instance_activities.xml @@ -0,0 +1,9 @@ + + + + | from datamodel:Cloud_Infrastructure.Compute | search dest=$dest$ | fields - _* + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml b/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml new file mode 100644 index 0000000000..eaacf46c7b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml @@ -0,0 +1,9 @@ + + + + | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_network_traffic_from_src_ip.xml b/package/default/data/ui/panels/workbench_panel_investigate_network_traffic_from_src_ip.xml new file mode 100644 index 0000000000..448e215f7d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_network_traffic_from_src_ip.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Network_Traffic.All_Traffic | search src_ip=$src_ip$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_app.xml b/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_app.xml new file mode 100644 index 0000000000..b7e6637221 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_app.xml @@ -0,0 +1,9 @@ + + + + eventtype=okta_log app=$app$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_ip_address.xml b/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_ip_address.xml new file mode 100644 index 0000000000..f702ab0a9b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_okta_activity_by_ip_address.xml @@ -0,0 +1,9 @@ + + + + eventtype=okta_log src_ip={src_ip} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_pass_the_hash_attempts.xml b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_hash_attempts.xml new file mode 100644 index 0000000000..b06e6c71c1 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_hash_attempts.xml @@ -0,0 +1,9 @@ + + + + `wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate | stats count earliest(_time) as first_login latest(_time) as last_login by src_user dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml new file mode 100644 index 0000000000..02d2dff20d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml @@ -0,0 +1,9 @@ + + + + `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest | where sum_count/max_count!=2 | rename new_user AS user + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml b/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml new file mode 100644 index 0000000000..ab5940c8fe --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml @@ -0,0 +1,9 @@ + + + + | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_successful_remote_desktop_authentications.xml b/package/default/data/ui/panels/workbench_panel_investigate_successful_remote_desktop_authentications.xml new file mode 100644 index 0000000000..fc1ff90dda --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_successful_remote_desktop_authentications.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")`| table firstTime lastTime src src_nt_domain dest user app count | sort count + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml b/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml new file mode 100644 index 0000000000..bb306f81a8 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml @@ -0,0 +1,9 @@ + + + + | search sourcetype=stream:http src_ip="$src_ip$" dest_ip="$dest_ip$" | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_all_cloud_regions.xml b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_all_cloud_regions.xml new file mode 100644 index 0000000000..78154b2139 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_all_cloud_regions.xml @@ -0,0 +1,9 @@ + + + + | from datamodel:Cloud_Infrastructure.Compute | search user=$src_user$ | fields - _* + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_okta.xml b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_okta.xml new file mode 100644 index 0000000000..0adc7b4f9c --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_okta.xml @@ -0,0 +1,9 @@ + + + + eventtype=okta_log user=$user$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_single_cloud_region.xml b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_single_cloud_region.xml new file mode 100644 index 0000000000..1aa8d0f2a3 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_user_activities_in_single_cloud_region.xml @@ -0,0 +1,9 @@ + + + + | from datamodel:Cloud_Infrastructure.Compute | search region=$region$ user=$src_user$ | fields - _* + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_host.xml b/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_host.xml new file mode 100644 index 0000000000..0c8c4e596d --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_host.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Web.Web | search src=$dest$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_src_ip.xml b/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_src_ip.xml new file mode 100644 index 0000000000..612d227d1b --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_web_activity_from_src_ip.xml @@ -0,0 +1,9 @@ + + + + | from datamodel Web.Web | search src=$src_ip$ + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_investigate_web_posts_from_src.xml b/package/default/data/ui/panels/workbench_panel_investigate_web_posts_from_src.xml new file mode 100644 index 0000000000..43d10b1103 --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_investigate_web_posts_from_src.xml @@ -0,0 +1,9 @@ + + + + | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "$src$") and like(http_method, "POST") + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/panels/workbench_panel_process_chain_analysis.xml b/package/default/data/ui/panels/workbench_panel_process_chain_analysis.xml new file mode 100644 index 0000000000..582373453a --- /dev/null +++ b/package/default/data/ui/panels/workbench_panel_process_chain_analysis.xml @@ -0,0 +1,9 @@ + + + + `sysmon` EventCode=1 NOT process=*Splunk* | rename process_guid AS out_process_guid process_name AS out_process_name parent_process_guid AS out_parent_process_guid parent_process_name AS out_parent_process_name | stats count by out_process_guid out_process_name out_parent_process_guid out_parent_process_name | eval join_process_guid = out_process_guid | join join_process_guid [ search `sysmon` process_guid={process_guid} EventCode=1 | rename process_name AS sub_process_name process_guid AS sub_process_guid parent_process_name AS sub_parent_process_name parent_process_guid AS sub_parent_process_guid | stats count by sub_process_name sub_process_guid sub_parent_process_name sub_parent_process_guid | eval join_process_guid = sub_parent_process_guid] | rename sub_process_guid AS process_guid sub_process_name AS process_name out_process_guid AS parent_process_guid out_process_name AS parent_process_name out_parent_process_guid AS grandparent_process_guid out_parent_process_name AS grandparent_process_name | stats count by process_guid process_name parent_process_guid parent_process_name grandparent_process_guid grandparent_process_name | head 1 | fields - count + + + +
+ \ No newline at end of file diff --git a/package/default/data/ui/views/escu_summary.xml b/package/default/data/ui/views/escu_summary.xml index 252379d94b..f06d1eac19 100644 --- a/package/default/data/ui/views/escu_summary.xml +++ b/package/default/data/ui/views/escu_summary.xml @@ -227,23 +227,6 @@ " * - - - All - Persistence - Privilege Escalation - Defense Evasion - Credential Access - Discovery - Lateral Movement - Execution - Collection - Exfiltration - Command and Control - " - " - * - All @@ -287,18 +270,15 @@ spath input=data_models path={} output="Data Models" | spath input=mappings path=kill_chain_phases{} output="Kill Chain Phases" | spath input=mappings path=cis20{} output=cis - | spath input=mappings path=mitre_attack{} output="ATT&CK" - | spath input=providing_technologies path={} output="Providing Technologies" | rex field=cis "CIS\s(?<num>\d+)" | rename num as CIS | rename title as "Analytic Story" description as "Description" category as "Category" creation_date as "Created" modification_date as "Last Updated" | fillnull value="-" | search "Data Models"=$as_data_models$ - | search "ATT&CK"=$as_mitre_attack$ | search "Kill Chain Phases"=$as_kill_chain_phase$ | search "Category"=$as_category$ | search CIS=$as_cis$ - | table "Analytic Story" Description Category "Kill Chain Phases" "ATT&CK", CIS, "Data Models" "Providing Technologies" "Created" "Last Updated" + | table "Analytic Story" Description Category "Kill Chain Phases" , CIS, "Data Models" "Created" "Last Updated" @@ -416,23 +396,7 @@ " * - - - All - Persistence - Privilege Escalation - Defense Evasion - Credential Access - Discovery - Lateral Movement - Execution - Collection - Exfiltration - Command and Control - " - " - * - + All @@ -476,17 +440,15 @@ spath input=action.escu.data_models path={} output="Data Models" | spath input=action.escu.mappings path=kill_chain_phases{} output="Kill Chain Phases" | spath input=action.escu.mappings path=cis20{} output=cis - | spath input=action.escu.mappings path=mitre_attack{} output="ATT&CK" | rex field=cis "CIS\s(?<num>\d+)" | rename num as CIS | fillnull value="-" | search CIS=$cis_token$ - | search "ATT&CK"=$mitre_attack$ | search "Data Models"=$data_models$ | search "Kill Chain Phases"=$kill_chain_phase$ | search action.escu.search_type=$searchtype$ | rename action.escu.asset_at_risk AS "Asset at Risk" | rename title as "Search Name"| rename modification_date as "Last Updated" - | table "Search Name" description "Kill Chain Phases" CIS "ATT&CK" "Data Models" "Last Updated" + | table "Search Name" description "Kill Chain Phases" CIS "Data Models" "Last Updated" 0 diff --git a/package/default/es_investigations.conf b/package/default/es_investigations.conf new file mode 100644 index 0000000000..8811fa4b84 --- /dev/null +++ b/package/default/es_investigations.conf @@ -0,0 +1,1489 @@ + +[panel_group://workbench_panel_group_aws_cross_account_activity] +label = AWS Cross Account Activity +description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. +disabled = 0 +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_source_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid"] + +[panel_group://workbench_panel_group_aws_cryptomining] +label = AWS Cryptomining +description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. +disabled = 0 +panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] + +[panel_group://workbench_panel_group_aws_network_acl_activity] +label = AWS Network ACL Activity +description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. +disabled = 0 +panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_aws_suspicious_provisioning_activities] +label = AWS Suspicious Provisioning Activities +description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. +disabled = 0 +panels = ["panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_all_aws_activity_from_region", "panel://workbench_panel_get_all_aws_activity_from_country", "panel://workbench_panel_get_all_aws_activity_from_city"] + +[panel_group://workbench_panel_group_aws_user_monitoring] +label = AWS User Monitoring +description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_aws_user_activities_by_user_field"] + +[panel_group://workbench_panel_group_account_monitoring_and_controls] +label = Account Monitoring and Controls +description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. +disabled = 0 +panels = ["panel://workbench_panel_get_logon_rights_modifications_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_logon_rights_modifications_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_apache_struts_vulnerability] +label = Apache Struts Vulnerability +description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_suspicious_strings_in_http_header", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_posts_from_src", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_asset_tracking] +label = Asset Tracking +description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address"] + +[panel_group://workbench_panel_group_brand_monitoring] +label = Brand Monitoring +description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. +disabled = 0 +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_cloud_cryptomining] +label = Cloud Cryptomining +description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. +disabled = 0 +panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_investigate_user_activities_in_single_cloud_region", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_user_activities_in_all_cloud_regions", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_investigate_cloud_compute_instance_activities", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] + +[panel_group://workbench_panel_group_coldroot_macos_rat] +label = ColdRoot MacOS RAT +description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. +disabled = 0 +panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_collection_and_staging] +label = Collection and Staging +description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_command_and_control] +label = Command and Control +description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. +disabled = 0 +panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_common_phishing_frameworks] +label = Common Phishing Frameworks +description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. +disabled = 0 +panels = ["panel://workbench_panel_get_certificate_logs_for_a_domain"] + +[panel_group://workbench_panel_group_container_implantation_monitoring_and_investigation] +label = Container Implantation Monitoring and Investigation +description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. +disabled = 0 +panels = ["panel://workbench_panel_investigate_aws_ecr_container_listing_activity"] + +[panel_group://workbench_panel_group_credential_dumping] +label = Credential Dumping +description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. +disabled = 0 +panels = ["panel://workbench_panel_investigate_failed_logins_for_multiple_destinations", "panel://workbench_panel_investigate_previous_unseen_user", "panel://workbench_panel_investigate_pass_the_hash_attempts", "panel://workbench_panel_investigate_pass_the_ticket_attempts"] + +[panel_group://workbench_panel_group_dhs_report_ta18_074a] +label = DHS Report TA18-074A +description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_file_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_dns_amplification_attacks] +label = DNS Amplification Attacks +description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. +disabled = 0 +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] + +[panel_group://workbench_panel_group_dns_hijacking] +label = DNS Hijacking +description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host"] + +[panel_group://workbench_panel_group_data_protection] +label = Data Protection +description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_disabling_security_tools] +label = Disabling Security Tools +description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_dynamic_dns] +label = Dynamic DNS +description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_emotet_malware__dhs_report_ta18_201a_] +label = Emotet Malware DHS Report TA18-201A +description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_hidden_cobra_malware] +label = Hidden Cobra Malware +description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_host_redirection] +label = Host Redirection +description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_jboss_vulnerability] +label = JBoss Vulnerability +description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info"] + +[panel_group://workbench_panel_group_kubernetes_scanning_activity] +label = Kubernetes Scanning Activity +description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. +disabled = 0 +panels = ["panel://workbench_panel_gcp_kubernetes_activity_by_src_ip", "panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info"] + +[panel_group://workbench_panel_group_lateral_movement] +label = Lateral Movement +description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. +disabled = 0 +panels = ["panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_malicious_powershell] +label = Malicious PowerShell +description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_monitor_backup_solution] +label = Monitor Backup Solution +description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. +disabled = 0 +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_all_backup_logs_for_host"] + +[panel_group://workbench_panel_group_monitor_for_unauthorized_software] +label = Monitor for Unauthorized Software +description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_monitor_for_updates] +label = Monitor for Updates +description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] + +[panel_group://workbench_panel_group_netsh_abuse] +label = Netsh Abuse +description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_orangeworm_attack_group] +label = Orangeworm Attack Group +description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_phishing_payloads] +label = Phishing Payloads +description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info"] + +[panel_group://workbench_panel_group_possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns] +label = Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_prohibited_traffic_allowed_or_protocol_mismatch] +label = Prohibited Traffic Allowed or Protocol Mismatch +description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_ransomware] +label = Ransomware +description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. +disabled = 0 +panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_router_and_infrastructure_security] +label = Router and Infrastructure Security +description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_sql_injection] +label = SQL Injection +description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] + +[panel_group://workbench_panel_group_samsam_ransomware] +label = SamSam Ransomware +description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. +disabled = 0 +panels = ["panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_spectre_and_meltdown_vulnerabilities] +label = Spectre And Meltdown Vulnerabilities +description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_splunk_enterprise_vulnerability] +label = Splunk Enterprise Vulnerability +description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] + +[panel_group://workbench_panel_group_splunk_enterprise_vulnerability_cve_2018_11409] +label = Splunk Enterprise Vulnerability CVE-2018-11409 +description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. +disabled = 0 +panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_notable_info"] + +[panel_group://workbench_panel_group_suspicious_aws_ec2_activities] +label = Suspicious AWS EC2 Activities +description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. +disabled = 0 +panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] + +[panel_group://workbench_panel_group_suspicious_aws_login_activities] +label = Suspicious AWS Login Activities +description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. +disabled = 0 +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn"] + +[panel_group://workbench_panel_group_suspicious_aws_s3_activities] +label = Suspicious AWS S3 Activities +description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. +disabled = 0 +panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname"] + +[panel_group://workbench_panel_group_suspicious_aws_traffic] +label = Suspicious AWS Traffic +description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). +disabled = 0 +panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_suspicious_command_line_executions] +label = Suspicious Command-Line Executions +description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_suspicious_dns_traffic] +label = Suspicious DNS Traffic +description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. +disabled = 0 +panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_suspicious_emails] +label = Suspicious Emails +description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_suspicious_mshta_activity] +label = Suspicious MSHTA Activity +description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_suspicious_okta_activity] +label = Suspicious Okta Activity +description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. +disabled = 0 +panels = ["panel://workbench_panel_investigate_user_activities_in_okta", "panel://workbench_panel_investigate_okta_activity_by_ip_address", "panel://workbench_panel_investigate_okta_activity_by_app"] + +[panel_group://workbench_panel_group_suspicious_wmi_use] +label = Suspicious WMI Use +description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. +disabled = 0 +panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_suspicious_windows_registry_activities] +label = Suspicious Windows Registry Activities +description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_unusual_aws_ec2_modifications] +label = Unusual AWS EC2 Modifications +description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. +disabled = 0 +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid"] + +[panel_group://workbench_panel_group_unusual_processes] +label = Unusual Processes +description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_use_of_cleartext_protocols] +label = Use of Cleartext Protocols +description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_web_fraud_detection] +label = Web Fraud Detection +description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_web_session_information_via_session_id"] + +[panel_group://workbench_panel_group_windows_defense_evasion_tactics] +label = Windows Defense Evasion Tactics +description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_windows_file_extension_and_association_abuse] +label = Windows File Extension and Association Abuse +description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_windows_log_manipulation] +label = Windows Log Manipulation +description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + +[panel_group://workbench_panel_group_windows_persistence_techniques] +label = Windows Persistence Techniques +description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_windows_privilege_escalation] +label = Windows Privilege Escalation +description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_windows_service_abuse] +label = Windows Service Abuse +description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. +disabled = 0 +panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] + + + +[panel://workbench_panel_aws_investigate_user_activities_by_arn] +label = AWS Investigate User Activities By ARN +description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid] +label = AWS Investigate User Activities By AccessKeyId +description = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific credentials. +disabled = 0 +tokens = {\ + "accessKeyId": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_aws_investigate_user_activities_by_source_user] +label = AWS Investigate User Activities By Source User +description = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific ARNs. +disabled = 0 +tokens = {\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_aws_network_acl_details_from_id] +label = AWS Network ACL Details from ID +description = This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID +disabled = 0 +tokens = {\ + "networkAclId": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_aws_network_interface_details_via_resourceid] +label = AWS Network Interface details via resourceId +description = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface. +disabled = 0 +tokens = {\ + "resourceId": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_aws_s3_bucket_details_via_bucketname] +label = AWS S3 Bucket details via bucketName +description = This search queries AWS configuration logs and returns the information about a specific S3 bucket. The information returned includes the time the S3 bucket was created, the resource ID, the region it belongs to, the value of action performed, AWS account ID, and configuration values of the access-control lists associated with the bucket. +disabled = 0 +tokens = {\ + "bucketName": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_all_backup_logs_for_host] +label = All backup logs for host +description = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip] +label = Amazon EKS Kubernetes activity by src ip +description = This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_gcp_kubernetes_activity_by_src_ip] +label = GCP Kubernetes activity by src ip +description = This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address +disabled = 0 +tokens = {\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_all_aws_activity_from_city] +label = Get All AWS Activity From City +description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled = 0 +tokens = {\ + "City": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_all_aws_activity_from_country] +label = Get All AWS Activity From Country +description = This search retrieves all the activity from a specific country and will create a table containing the time, country, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled = 0 +tokens = {\ + "Country": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_all_aws_activity_from_ip_address] +label = Get All AWS Activity From IP Address +description = This search retrieves all the activity from a specific IP address and will create a table containing the time, ARN, username, the type of user, the IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_all_aws_activity_from_region] +label = Get All AWS Activity From Region +description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled = 0 +tokens = {\ + "Region": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_authentication_logs_for_endpoint] +label = Get Authentication Logs For Endpoint +description = This search returns all users that have attempted to access a particular endpoint. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_backup_logs_for_endpoint] +label = Get Backup Logs For Endpoint +description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_certificate_logs_for_a_domain] +label = Get Certificate logs for a domain +description = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. +disabled = 0 +tokens = {\ + "domain": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_dns_server_history_for_a_host] +label = Get DNS Server History for a host +description = While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_dns_traffic_ratio] +label = Get DNS traffic ratio +description = This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_ec2_instance_details_by_instanceid] +label = Get EC2 Instance Details by instanceId +description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field +disabled = 0 +tokens = {\ + "instanceId": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_ec2_launch_details] +label = Get EC2 Launch Details +description = This search returns some of the launch details for a EC2 instance. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_email_info] +label = Get Email Info +description = This search returns all the information Splunk might have collected a specific email message over the last 2 hours. +disabled = 0 +tokens = {\ + "message_id": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_emails_from_specific_sender] +label = Get Emails From Specific Sender +description = This search returns all the emails from a specific sender over the last 24 and next hours. +disabled = 0 +tokens = {\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address] +label = Get First Occurrence and Last Occurrence of a MAC Address +description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. +disabled = 0 +tokens = {\ + "src_mac": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_history_of_email_sources] +label = Get History Of Email Sources +description = This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source. +disabled = 0 +tokens = {\ + "src": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_logon_rights_modifications_for_endpoint] +label = Get Logon Rights Modifications For Endpoint +description = This search allows you to retrieve any modifications to logon rights associated with a specific host. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_logon_rights_modifications_for_user] +label = Get Logon Rights Modifications For User +description = This search allows you to retrieve any modifications to logon rights for a specific user account. +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_notable_history] +label = Get Notable History +description = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_notable_info] +label = Get Notable Info +description = This search queries the notable index to retrieve detailed information captured within the notable. Every notable has a unique ID associated with it, which is used to point us directly to the notable event under investigation. +disabled = 0 +tokens = {\ + "event_id": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_outbound_emails_to_hidden_cobra_threat_actors] +label = Get Outbound Emails to Hidden Cobra Threat Actors +description = This search returns the information of the users that sent emails to the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, and from `redhat@gmail.com`. +disabled = 0 +tokens = {\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "recipient": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_parent_process_info] +label = Get Parent Process Info +description = This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest +disabled = 0 +tokens = {\ + "process_name": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_process_file_activity] +label = Get Process File Activity +description = This search returns the file activity for a specific process on a specific endpoint +disabled = 0 +tokens = {\ + "process_id": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_process_info] +label = Get Process Info +description = This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address. +disabled = 0 +tokens = {\ + "process_name": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_process_information_for_port_activity] +label = Get Process Information For Port Activity +description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. +disabled = 0 +tokens = {\ + "dest_port": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_process_registry_activity] +label = Get Process Registry Activity +description = This search returns the registry activity for a specific process on a specific endpoint +disabled = 0 +tokens = {\ + "process_id": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_process_responsible_for_the_dns_traffic] +label = Get Process Responsible For The DNS Traffic +description = While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_registry_activities] +label = Get Registry Activities +description = This search queries the Endpoint Datamodel to give you details of the latest registry values for a specific destination computer. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_risk_modifiers_for_endpoint] +label = Get Risk Modifiers For Endpoint +description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk\_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_risk_modifiers_for_user] +label = Get Risk Modifiers For User +description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk_scores, names of the correlation searches that contributed to create a risk score for a specific user +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_sysmon_wmi_activity_for_host] +label = Get Sysmon WMI Activity for Host +description = This search queries Sysmon WMI events for the host of interest. +disabled = 0 +tokens = {\ + "process": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_update_logs_for_endpoint] +label = Get Update Logs For Endpoint +description = This search will tell you give you the update logs for a specific endpoint for the last week. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_user_information_from_identity_table] +label = Get User Information from Identity Table +description = Gather more information about the user identified in the Notable Event. +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_vulnerability_logs_for_endpoint] +label = Get Vulnerability Logs For Endpoint +description = This search will show you any vulnerabilities noted for a specific endpoint for the last week. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_get_web_session_information_via_session_id] +label = Get Web Session Information via session id +description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. +disabled = 0 +tokens = {\ + "session_id": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_aws_ecr_container_listing_activity] +label = Investigate AWS ECR container listing activity +description = This search lists all the users performing a list image operation on AWS Elastic Container Registry. Listing source user, image id, source IP, user type, http user agent. This search also gives counts of unique user agents per listing source. +disabled = 0 +tokens = {\ + "Compute.event_name": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_aws_user_activities_by_user_field] +label = Investigate AWS User Activities by user field +description = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_aws_activities_via_region_name] +label = Investigate AWS activities via region name +description = This search lists all the user activities logged by CloudTrail for a specific region in question and will create a table of the values of parameters requested, the type of the event and the response from the AWS API by each user +disabled = 0 +tokens = {\ + "awsRegion": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_cloud_compute_instance_activities] +label = Investigate Cloud Compute Instance Activities +description = This search returns a logs of events that operated on the compute instance. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_failed_logins_for_multiple_destinations] +label = Investigate Failed Logins for Multiple Destinations +description = This search returns failed logins to multiple destinations by user. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_network_traffic_from_src_ip] +label = Investigate Network Traffic From src ip +description = This search allows you to find all the network traffic from a specific IP address. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_okta_activity_by_ip_address] +label = Investigate Okta Activity by IP Address +description = This search returns all okta events from a specific IP address. +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_okta_activity_by_app] +label = Investigate Okta Activity by app +description = This search returns all okta events associated with a specific app +disabled = 0 +tokens = {\ + "app": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_pass_the_hash_attempts] +label = Investigate Pass the Hash Attempts +description = This search hunts for dumped NTLM hashes used for pass the hash. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_pass_the_ticket_attempts] +label = Investigate Pass the Ticket Attempts +description = This search hunts for dumped kerberos ticket from LSASS memory. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_previous_unseen_user] +label = Investigate Previous Unseen User +description = This search returns previous unseen user, which didn't log in for 30 days. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_successful_remote_desktop_authentications] +label = Investigate Successful Remote Desktop Authentications +description = This search returns the source, destination, and user for all successful remote-desktop authentications. A successful authentication after a brute-force attack on a destination machine is suspicious behavior. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_suspicious_strings_in_http_header] +label = Investigate Suspicious Strings in HTTP Header +description = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "dest_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_user_activities_in_all_cloud_regions] +label = Investigate User Activities In All Cloud Regions +description = This search lists all the logged cloud infrastructure activities by a specific cloud user +disabled = 0 +tokens = {\ + "region": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_user_activities_in_okta] +label = Investigate User Activities In Okta +description = This search returns all okta events by a specific user +disabled = 0 +tokens = {\ + "user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "identity",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_user_activities_in_single_cloud_region] +label = Investigate User Activities In Single Cloud Region +description = This search lists all the logged cloud infrastructure activities by a specific cloud user in a specific cloud region +disabled = 0 +tokens = {\ + "region": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + },\ + "src_user": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_web_activity_from_host] +label = Investigate Web Activity From Host +description = This search allows you to find all the web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. +disabled = 0 +tokens = {\ + "dest": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_web_activity_from_src_ip] +label = Investigate Web Activity From src ip +description = This search searches for all web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. +disabled = 0 +tokens = {\ + "src_ip": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_investigate_web_posts_from_src] +label = Investigate Web POSTs From src +description = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. +disabled = 0 +tokens = {\ + "src": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + +[panel://workbench_panel_process_chain_analysis] +label = Process Chain Analysis +description = Analyze the Process Chain and identify the malicious file. By analyzing the parent process guid and searching for the process guid, the spawning process chain can be identified. +disabled = 0 +tokens = {\ + "process_guid": {\ + "valuePrefix": "",\ + "valueSuffix": "",\ + "delimiter": " OR ",\ + "valueType": "primitive",\ + "value": "asset",\ + "default": "_1!=1"\ + }\ +}\ + + diff --git a/package/default/macros.conf b/package/default/macros.conf index 4d1ab99d3e..53e8b92870 100644 --- a/package/default/macros.conf +++ b/package/default/macros.conf @@ -1,26 +1,18 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-05-25T14:45:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# -[access_lsass_memory_for_dump_creation_filter] -definition = search * -description = Use this macro to add additional filters for access lsass memory for dump creation +[aws_cloudwatchlogs_eks] +definition = sourcetype="aws:cloudwatchlogs:eks" +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. -[attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter] -definition = search * -description = Use this macro to add additional filter for attempt to set default powershell execution policy to unrestricted or bypass - -[attempted_credential_dump_from_registry_via_reg_filter] -definition = search * -description = Use this macro to add additional filter for attempted credential dump from registry via reg - -[attrib_to_hide_files_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. +[aws_s3_accesslogs] +definition = sourcetype=aws:s3:accesslogs +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [brand_abuse_dns] definition = lookup update=true brandMonitoring_lookup domain as query OUTPUT domain_abuse | search domain_abuse=true @@ -34,94 +26,30 @@ description = This macro limits the output to only domains that are in the brand definition = lookup update=true brandMonitoring_lookup domain as urls OUTPUT domain_abuse | search domain_abuse=true description = This macro limits the output to only domains that are in the brand monitoring lookup file -[change_file_association_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[children_of_spoolsv_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - [clients_connecting_to_multiple_dns_servers_output_filter] definition = search * description = Use this macro to add additional filters for monitoring clients connecting to multiple dns servers -[common_ransomware_extensions_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. +[cloudtrail] +definition = sourcetype=aws:cloudtrail +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. -[common_ransomware_notes_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. +[cloudwatch_eks] +definition = sourcetype="aws:cloudwatchlogs:eks" +description = customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch eks logs. Replace the macro definition with configurations for your Splunk Environmnent. -[container_implant_aws_detection_filter] -definition = search * -description = Use this macro to add additional filters +[cloudwatch_vpc] +definition = sourcetype=aws:cloudwatchlogs:vpcflow +description = customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch vpc logs. Replace the macro definition with configurations for your Splunk Environmnent. -[container_implant_gcp_detection_filter] -definition = search * -description = Use this macro to add additional filters - -[create_local_admin_via_net_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. +[cloudwatchlogs_vpcflow] +definition = sourcetype=aws:cloudwatchlogs:vpcflow +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [create_or_delete_windows_shares_filter] definition = search * description = Use this macro to add additional filter for create or delete windows shares -[create_remote_thread_into_lsass_filter] -definition = search * -description = Use this macro to add additional filter for create remote thread into lsass - -[creation_of_shadow_copy_filter] -definition = search * -description = Use this macro to add additional filter for creation of shadow copy - -[creation_of_shadow_copy_with_wmic_and_powershell_filter] -definition = search * -description = Use this macro to add additional filter for creation of shadow copy with wmic and powershell - -[cred_dump_via_copy_from_shadowcopy_filter] -definition = search * -description = Use this macro to add additional filter for cred dump via copy from shadowcopy - -[cred_dump_via_symlink_shadowcopy_filter] -definition = search * -description = Use this macro to add additional filter for cred dump via symlink shadowcopy - -[detect_activity_related_to_pass_the_hash_attacks_filter] -definition = search * -description = Use this macro to add additional filters for detect activity related to pass the hash attacks - -[detect_credential_dumping_through_LSASS_access_filter] -definition = search * -description = Use this macro to add additional filter for detect credential dumping through LSASS access - -[detect_mimikatz_using_loaded_images_filter] -definition = search * -description = Use this macro to add additional filter for detect mimikatz using loaded images - -[dns_query_length_mltk_filter] -definition = search * -description = Use this macro to add additional filters for monitoring DNS query length using MLTK - -[dns_query_length_with_high_standard_deviation_filter] -definition = search * -description = Use this macro to add additional filters for monitoring DNS query length with high standard deviation - -[dns_record_changed_filter] -definition = search * -description = Use this macro to add additional filters for monitoring DNS records changed - -[dragonfly_schtasks_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[dump_lsass_via_comsvcs_dll_filter] -definition = search * -description = Use this macro to add additional filter for dump lsass via comsvcs dll filter - [dynamic_dns_providers] definition = lookup update=true dynamic_dns_providers_default dynamic_dns_domains as query OUTPUTNEW isDynDNS_default | lookup update=true dynamic_dns_providers_local dynamic_dns_domains as query OUTPUTNEW isDynDNS_local| eval isDynDNS = coalesce(isDynDNS_default, isDynDNS_local)|fields - isDynDNS_default, isDynDNS_local| search isDynDNS=True description = This macro limits the output of the query field to dynamic dns domains. It looks up the domains in a file provided by Splunk and one intended to be updated by the end user. @@ -130,22 +58,10 @@ description = This macro limits the output of the query field to dynamic dns dom definition = lookup update=true dynamic_dns_providers_default dynamic_dns_domains as url OUTPUTNEW isDynDNS_default | lookup update=true dynamic_dns_providers_local dynamic_dns_domains as url OUTPUTNEW isDynDNS_local| eval isDynDNS = coalesce(isDynDNS_default, isDynDNS_local)|fields - isDynDNS_default, isDynDNS_local| search isDynDNS=True description = This is a description -[ec2_excessive_runinstances_mltk_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring ec2 runinstances [eg - src_user != 'someUserNameExperiencingFalsePositives']. - -[ec2_excessive_terminateinstances_mltk_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring ec2 terminateinstances [eg - src_user != 'someUserNameExperiencingFalsePositives']. - [ec2_modification_api_calls] definition = (eventName=AssociateAddress OR eventName=AssociateIamInstanceProfile OR eventName=AttachClassicLinkVpc OR eventName=AttachNetworkInterface OR eventName=AttachVolume OR eventName=BundleInstance OR eventName=DetachClassicLinkVpc OR eventName=DetachVolume OR eventName=GetConsoleOutput OR eventName=GetConsoleScreenshot OR eventName=ModifyInstanceAttribute OR eventName=ModifyInstancePlacement OR eventName=MonitorInstances OR eventName=RebootInstances OR eventName=ResetInstanceAttribute OR eventName=StartInstances OR eventName=StopInstances OR eventName=TerminateInstances OR eventName=UnmonitorInstances) description = This is a list of AWS event names that have to do with modifying Amazon EC2 instances -[email_files_not_in_outlook_directory_filter] -definition = search * -description = Use this macro to add additional filters to exclude false positive results - [evilginx_phishlets_0365] definition = (query=login* AND query=www*) description = This limits the query fields to domains that are associated with evilginx masquerading as Office 365 @@ -174,98 +90,46 @@ description = This limits the query fields to domains that are associated with e definition = (query=outlook* AND query=login* AND query=account*) description = This limits the query fields to domains that are associated with evilginx masquerading as Outlook -[file_write_spikes_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - [filter_rare_process_whitelist] definition = lookup update=true lookup_rare_process_whitelist_default process as process OUTPUTNEW whitelist | where whitelist="false" | lookup update=true lookup_rare_process_whitelist_local process as process OUTPUT whitelist | where whitelist="false" description = This macro is intended to whitelist processes that have been definied as rare -[first_time_seen_cmd_line_filter] -definition = search * -description = Use this macro to add additional filters for first time seen command line, you can exclude legitimate parent_processes that spawn cmd.exe. Eg- seach parent_process_name != "foo" +[google_gcp_pubnet_message] +definition = sourcetype="google:gcp:pubsub:message" +description = customer specific splunk configurations(eg- index, source, sourcetype) for Google GCP. Replace the macro definition with configurations for your Splunk Environmnent. + +[google_gcp_pubsub_message] +definition = sourcetype="google:gcp:pubsub:message" +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [is_windows_system_file] definition = lookup update=true is_windows_system_file filename as process_name OUTPUT systemFile | search systemFile=true description = This macro limits the output to process names that are in the Windows System directory -[kubernetes_aws_scan_fingerprint_detection] -definition = search * -description = Use this macro to add additional filters - -[kubernetes_gcp_scan_fingerprint_detection] -definition = search * -description = Use this macro to add additional filters - -[kubernetes_pods_aws_scan_fingerprint_detection] -definition = search * -description = Use this macro to add additional filters - -[lnk_executing_a_process_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[malicious_powershell_process_obfuscation_techniques_output_filter] -definition = search * -description = Use this macro to add output filters for Malicious PowerShell Process With Obfuscation Techniques. +[netbackup] +definition = sourcetype="netbackup_logs" +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [network_acl_events] definition = (eventName = CreateNetworkAcl OR eventName = CreateNetworkAclEntry OR eventName = DeleteNetworkAcl OR eventName = DeleteNetworkAclEntry OR eventName = ReplaceNetworkAclEntry OR eventName = ReplaceNetworkAclAssociation) description = This is a list of AWS event names that are associated with Network ACLs -[okta_failed_sso_attempt_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[okta_multiple_users_with_auth_failures_from_ip_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[okta_user_lockouts_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[okta_user_logins_from_multiple_cities_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[outbound_smb_connections_filter] -definition = search * -description = Use this macro to add additional filters for outbound SMB traffic detection - -[outlook_writing_zip_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - -[previously_seen_cloud_compute_creations_by_user_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring users that create cloud compute images +[okta] +definition = eventtype=okta_log +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [previously_seen_cloud_compute_creations_by_user_search_window_begin_offset] definition = -70m@m description = Use this macro to determine how far into the past the window should be to determine if the user is new or not -[previously_seen_cloud_compute_image_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring cloud compute images - [previously_seen_cloud_compute_image_search_window_begin_offset] definition = -70m@m description = Use this macro to determine how far into the past the window should be to determine if the image is new or not -[previously_seen_cloud_compute_instance_types_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring cloud compute instance types - [previously_seen_cloud_compute_instance_types_search_window_begin_offset] definition = -70m@m description = Use this macro to determine how far into the past the window should be to determine if the instance type is new or not -[previously_seen_cloud_regions_input_filter] -definition = search * -description = Use this macro to add additional filters for monitoring your cloud regions - [previously_seen_cloud_regions_search_window_begin_offset] definition = -70m@m description = Use this macro to determine how far into the past the window should be to determine if the region is new or not @@ -286,25 +150,13 @@ description = This macro limits the output to files that have extensions associa definition = lookup ransomware_notes_lookup ransomware_notes as file_name OUTPUT status as "Known Ransomware Notes" | search "Known Ransomware Notes"=True description = This macro limits the output to files that have been identified as a ransomware note -[rare_executables_on_endpoint_filter] -definition = search * -description = Update this macro to filter out false positives - -[remote_desktop_network_traffic_filter] -definition = search * -description = Use this macro to add additional filters for remote desktop network traffic - -[remote_desktop_process_running_on_system_filter] -definition = search * -description = Use this macro to add additional filters for remote desktop process running on system - [remove_valid_domains] definition = eval domain=trim(domain,"*") | search NOT[| inputlookup domains] NOT[ |inputlookup cim_corporate_email_domain_lookup] NOT[inputlookup cim_corporate_web_domain_lookup] | eval domain="*"+domain+"*" description = This macro removes valid domains from the output -[schtasks_scheduling_job_on_remote_system_filter] -definition = search * -description = Use this macro to add additional filters for schtasks scheduling job on remote system +[s3_accesslogs] +definition = sourcetype=aws:s3:accesslogs +description = customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch vpc logs. Replace the macro definition with configurations for your Splunk Environmnent. [security_content_ctime(1)] args = field @@ -319,71 +171,795 @@ description = search data model's summaries only definition = (eventName=AuthorizeSecurityGroupIngress OR eventName=CreateSecurityGroup OR eventName=DeleteSecurityGroup OR eventName=DescribeClusterSecurityGroups OR eventName=DescribeDBSecurityGroups OR eventName=DescribeSecurityGroupReferences OR eventName=DescribeSecurityGroups OR eventName=DescribeStaleSecurityGroups OR eventName=RevokeSecurityGroupIngress OR eventName=UpdateSecurityGroupRuleDescriptionsIngress) description = This macro is a list of AWS event names associated with security groups -[smb_traffic_spike_filter] -definition = search * -description = Use this macro to add additional filters for SMB traffic spike detection - -[smb_traffic_spike_mltk_filter] -definition = search * -description = Use this macro to add additional filters for SMB traffic spike detection using MLKT +[stream_http] +definition = sourcetype=stream:http +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [suspicious_email_attachments] definition = lookup update=true is_suspicious_file_extension_lookup file_name OUTPUT suspicious | search suspicious=true description = This macro limits the output to email attachments that have suspicious extensions -[suspicious_email_attachments_filter] -definition = search * -description = Use this macro to add additional filters for suspicious email attachments - -[suspicious_reg_process_filter] -definition = search * -description = Update this macro to limit the output results to filter out false positives. - [suspicious_writes] definition = lookup suspicious_writes_lookup file as file_name OUTPUT note as "Reference" | search "Reference" != False description = This macro limites the output to file names that have been marked as suspicious [sysmon] -definition = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" -description = customer specific splunk configurations(eg- index, source, sourcetype) for Windows Sysmon Logs. Replace the macro definition with configurations for your Splunk Environmnent. +definition = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [system_network_configuration_discovery_tools] definition = (process_name= "arp.exe" OR process_name= "at.exe" OR process_name= "attrib.exe" OR process_name= "cscript.exe" OR process_name= "dsquery.exe" OR process_name= "hostname.exe" OR process_name= "ipconfig.exe" OR process_name= "mimikatz.exe" OR process_name= "nbstat.exe" OR process_name= "net.exe" OR process_name= "netsh.exe" OR process_name= "nslookup.exe" OR process_name= "ping.exe" OR process_name= "quser.exe" OR process_name= "qwinsta.exe" OR process_name= "reg.exe" OR process_name= "runas.exe" OR process_name= "sc.exe" OR process_name= "schtasks.exe" OR process_name= "ssh.exe" OR process_name= "systeminfo.exe" OR process_name= "taskkill.exe" OR process_name= "telnet.exe" OR process_name= "tracert.exe" OR process_name="wscript.exe" OR process_name= "xcopy.exe") description = This macro is a list of process that can be used to discover the network configuration -[unauthorized_dns_servers_filter] -definition = search * -description = Use this macro to add additional filters for DNS Query Requests Resolved by Unauthorized DNS Servers detection - [uncommon_processes] definition = lookup update=true lookup_uncommon_processes_default process_name as process_name outputnew uncommon_default,category_default,analytic_story_default,kill_chain_phase_default,mitre_attack_default | lookup update=true lookup_uncommon_processes_local process_name as process_name outputnew uncommon_local,category_local,analytic_story_local,kill_chain_phase_local,mitre_attack_local | eval uncommon = coalesce(uncommon_default, uncommon_local), analytic_story = coalesce(analytic_story_default, analytic_story_local), category=coalesce(category_default, category_local), kill_chain_phase=coalesce(kill_chain_phase_default, kill_chain_phase_local), mitre_attack=coalesce(mitre_attack_default, mitre_attack_local) | fields - analytic_story_default, analytic_story_local, category_default, category_local, kill_chain_phase_default, kill_chain_phase_local, mitre_attack_default, mitre_attack_local, uncommon_default, uncommon_local | search uncommon=true description = This macro limits the output to processes that have been marked as uncommon -[uncommon_processes_filter] +[wineventlog_security] +definition = eventtype=wineventlog_security +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. + +[wineventlog_system] +definition = eventtype=wineventlog_system +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. + +[wmi] +definition = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. + +[aws_cloud_provisioning_from_previously_unseen_city_filter] definition = search * -description = Update this macro to limit the output results to filter out false positives. +description = Update this macro to limit the output results to filter out false positives. + +[aws_cloud_provisioning_from_previously_unseen_country_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[aws_cloud_provisioning_from_previously_unseen_ip_address_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[aws_cloud_provisioning_from_previously_unseen_region_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[aws_cross_account_activity_from_previously_unseen_account_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[aws_network_access_control_list_created_with_all_open_ports_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[aws_network_access_control_list_deleted_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[abnormally_high_aws_instances_launched_by_user_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[abnormally_high_aws_instances_launched_by_user___mltk_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[abnormally_high_aws_instances_terminated_by_user_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[abnormally_high_aws_instances_terminated_by_user___mltk_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[access_lsass_memory_for_dump_creation_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[amazon_eks_kubernetes_pod_scan_detection_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[amazon_eks_kubernetes_cluster_scan_detection_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[attempt_to_add_certificate_to_untrusted_store_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[attempt_to_stop_security_service_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[attempted_credential_dump_from_registry_via_reg_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[batch_file_write_to_system32_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[child_processes_of_spoolsv_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[clients_connecting_to_multiple_dns_servers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[cloud_compute_instance_created_by_previously_unseen_user_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[cloud_compute_instance_created_with_previously_unseen_image_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[cloud_compute_instance_created_with_previously_unseen_instance_type_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[cloud_compute_instance_started_in_previously_unused_region_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[common_ransomware_extensions_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[common_ransomware_notes_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[create_remote_thread_into_lsass_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[create_local_admin_accounts_using_net_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[create_or_delete_windows_shares_using_net_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[creation_of_shadow_copy_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[creation_of_shadow_copy_with_wmic_and_powershell_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[credential_dumping_via_copy_command_from_shadow_copy_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[credential_dumping_via_symlink_to_shadow_copy_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[dns_query_length_outliers___mltk_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[dns_query_length_with_high_standard_deviation_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[dns_query_requests_resolved_by_unauthorized_dns_servers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[dns_record_changed_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[deleting_shadow_copies_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_api_activity_from_users_without_mfa_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_aws_api_activities_from_unapproved_accounts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_aws_console_login_by_user_from_new_city_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_aws_console_login_by_user_from_new_country_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_aws_console_login_by_user_from_new_region_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_activity_related_to_pass_the_hash_attacks_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_credential_dumping_through_lsass_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_excessive_account_lockouts_from_endpoint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_excessive_user_account_lockouts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_large_outbound_icmp_packets_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_long_dns_txt_record_response_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_mimikatz_using_loaded_images_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_mimikatz_via_powershell_and_eventcode_4703_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_new_local_admin_account_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_new_login_attempts_to_routers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_new_open_s3_buckets_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_oulook_exe_writing_a__zip_file_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_outbound_smb_traffic_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_path_interception_by_creation_of_program_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_prohibited_applications_spawning_cmd_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_psexec_with_accepteula_flag_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_rare_executables_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_s3_access_from_a_new_ip_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_spike_in_aws_api_activity_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_spike_in_network_acl_activity_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_spike_in_s3_bucket_deletion_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_spike_in_security_group_activity_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_spike_in_blocked_outbound_traffic_from_your_aws_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_usb_device_insertion_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_unauthorized_assets_by_mac_address_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_use_of_cmd_exe_to_launch_script_interpreters_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_attackers_scanning_for_vulnerable_jboss_servers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_hosts_connecting_to_dynamic_domain_providers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_malicious_requests_to_exploit_jboss_servers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_mshta_exe_running_scripts_in_command_line_arguments_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_new_api_calls_from_user_roles_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_new_user_aws_console_login_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_processes_used_for_system_network_configuration_discovery_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detect_web_traffic_to_dynamic_domain_providers_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detection_of_dns_tunnels_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[detection_of_tools_built_by_nirsoft_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[disabling_remote_user_account_control_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[dump_lsass_via_comsvcs_dll_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[ec2_instance_modified_with_previously_unseen_user_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[ec2_instance_started_in_previously_unseen_region_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[ec2_instance_started_with_previously_unseen_ami_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[ec2_instance_started_with_previously_unseen_instance_type_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[ec2_instance_started_with_previously_unseen_user_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[email_attachments_with_lots_of_spaces_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[email_files_written_outside_of_the_outlook_directory_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[email_servers_sending_high_volume_traffic_to_hosts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[excessive_dns_failures_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[execution_of_file_with_spaces_before_extension_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[execution_of_file_with_multiple_extensions_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[extended_period_without_successful_netbackup_backups_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[file_with_samsam_extension_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[first_time_seen_running_windows_service_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[first_time_seen_command_line_argument_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[gcp_gcr_container_uploaded_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[gcp_kubernetes_cluster_scan_detection_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[hiding_files_and_directories_with_attrib_exe_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[hosts_receiving_high_volume_of_network_traffic_from_email_server_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[identify_new_user_accounts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[large_volume_of_dns_any_queries_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[macos___re_opened_applications_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[malicious_powershell_process___connect_to_internet_with_hidden_window_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[malicious_powershell_process___encoded_command_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[malicious_powershell_process___execution_policy_bypass_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[malicious_powershell_process___multiple_suspicious_command_line_arguments_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[malicious_powershell_process_with_obfuscation_techniques_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[monitor_dns_for_brand_abuse_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[monitor_email_for_brand_abuse_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[monitor_registry_keys_for_print_monitors_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[monitor_web_traffic_for_brand_abuse_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[multiple_okta_users_with_invalid_credentails_from_the_same_ip_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[new_container_uploaded_to_aws_ecr_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[no_windows_updates_in_a_time_frame_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[okta_account_lockout_events_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[okta_failed_sso_attempts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[okta_user_logins_from_multiple_cities_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[open_redirect_in_splunk_web_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[osquery_pack___coldroot_detection_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[overwriting_accessibility_binaries_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[process_execution_via_wmi_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[processes_tapping_keyboard_events_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[processes_created_by_netsh_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[processes_launching_netsh_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[prohibited_network_traffic_allowed_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[prohibited_software_on_endpoint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[protocol_or_port_mismatch_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[protocols_passing_authentication_in_cleartext_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[reg_exe_manipulating_windows_services_registry_keys_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[reg_exe_used_to_hide_files_directories_via_registry_keys_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[registry_keys_used_for_persistence_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[registry_keys_used_for_privilege_escalation_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[registry_keys_for_creating_shim_databases_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_desktop_network_bruteforce_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_desktop_network_traffic_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_desktop_process_running_on_system_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_process_instantiation_via_wmi_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_registry_key_modifications_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[remote_wmi_command_attempt_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[rundll_loading_dll_by_ordinal_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[smb_traffic_spike_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[smb_traffic_spike___mltk_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[sql_injection_with_long_urls_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[samsam_test_file_write_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[sc_exe_manipulating_windows_services_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[scheduled_task_name_used_by_dragonfly_threat_actors_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[scheduled_tasks_used_in_badrabbit_ransomware_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[schtasks_scheduling_job_on_remote_system_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[schtasks_used_for_forcing_a_reboot_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[script_execution_via_wmi_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[shim_database_file_creation_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[shim_database_installation_with_suspicious_parameters_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[short_lived_windows_accounts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[single_letter_process_on_endpoint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[spectre_and_meltdown_vulnerable_systems_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[spike_in_file_writes_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[splunk_enterprise_information_disclosure_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_changes_to_file_associations_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_email___uba_anomaly_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_email_attachment_extensions_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_file_write_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_java_classes_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_lnk_file_launching_a_process_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_reg_exe_process_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_wevtutil_usage_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_writes_to_system_volume_information_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[suspicious_writes_to_windows_recycle_bin_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[system_processes_run_from_unexpected_locations_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[tor_traffic_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[usn_journal_deletion_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[uncommon_processes_on_endpoint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. [unload_sysmon_filter_driver_filter] definition = search * -description = Use this macro to add additional filter for Unload sysmon filter driver search to filter out the false positives +description = Update this macro to limit the output results to filter out false positives. -[unsigned_image_loaded_by_LSASS_filter] +[unsigned_image_loaded_by_lsass_filter] definition = search * -description = Use this macro to add additional filter for unsigned image loaded by LSASS +description = Update this macro to limit the output results to filter out false positives. -[unusually_long_commandlines_filter] +[unsuccessful_netbackup_backups_filter] definition = search * -description = Update this macro to limit the output results to filter out false positives. +description = Update this macro to limit the output results to filter out false positives. -[wineventlog_security] -definition = eventtype="wineventlog_security" -description = Customer specific splunk configurations(eg- index, source, sourcetype) for Windows Event Logs. Replace the macro definition with configurations for your Splunk Environmnent. - -[wmi_process_launch_filter] +[unusually_long_command_line_filter] definition = search * -description = Update this macro to limit the output results to filter out false positives. +description = Update this macro to limit the output results to filter out false positives. -[wmi_script_execution_filter] +[unusually_long_command_line___mltk_filter] definition = search * -description = Update this macro to limit the output results to filter out false positives. +description = Update this macro to limit the output results to filter out false positives. + +[unusually_long_content_type_length_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[wmi_permanent_event_subscription_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[wmi_permanent_event_subscription___sysmon_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[wmi_temporary_event_subscription_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[web_fraud___account_harvesting_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[web_fraud___anomalous_user_clickspeed_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[web_fraud___password_sharing_across_accounts_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[web_servers_executing_suspicious_processes_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[windows_event_log_cleared_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[windows_hosts_file_modification_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. diff --git a/package/default/savedsearches.conf b/package/default/savedsearches.conf index 6b2ff03a98..f402d27b79 100644 --- a/package/default/savedsearches.conf +++ b/package/default/savedsearches.conf @@ -1,52 +1,42 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-05-25T14:45:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# ### ESCU DETECTIONS ### - [ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +action.escu.data_models = [] +action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen City +action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, city -action.notable.param.rule_description = Your AWS infrastructure was provisioned from a city, $city$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $city$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." +action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen City action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -55,49 +45,39 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +action.escu.data_models = [] +action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen Country +action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, country -action.notable.param.rule_description = Your AWS infrastructure was provisioned from a country, $country$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $country$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." +action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Country action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -106,49 +86,39 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +action.escu.data_models = [] +action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address +action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $src_ip$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." +action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen IP Address action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -157,49 +127,39 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode +search = `cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +action.escu.data_models = [] +action.escu.eli5 = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen Region +action.correlationsearch.label = ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, Region -action.notable.param.rule_description = Your AWS infrastructure was provisioned from a region, $Region$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $region$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." +action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Region action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -208,57 +168,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` [ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} -action.escu.eli5 = This search\ -1. Retrieves the **AssumeRole** event\ -1. Verifies that the log entry contains a value for the account ID of the requesting account\ -1. Ensures that the requesting account ID does not match the account ID of the requested account\ -1. Pulls in the previously seen requesting and requested account IDs\ -1. Splits up and executes multiple search paths at the same.\ -1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ -1. Outputs the data to the cache file.\ -1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ -The search finishes by gathering the data that it will display to the user. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -action.escu.creation_date = 2018-02-01 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest_user"] -action.escu.entities = ["dest_user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cross Account Activity"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cross Account Activity From Previously Unseen Account +action.correlationsearch.label = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = requestingAccountId, requestedAccountId, src_user, dest_user -action.notable.param.rule_description = Access to $dest_user$ was requested for the first time by $src_user$ -action.notable.param.rule_title = AWS Account $dest_user$ access by $src_user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. +action.notable.param.rule_title = AWS Cross Account Activity From Previously Unseen Account action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = requestingAccountId, requestedAccountId -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -267,48 +208,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID +search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID | `aws_cross_account_activity_from_previously_unseen_account_filter` [ESCU - AWS Network Access Control List Created with All Open Ports - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. -action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = A network access control list (ACL) is a layer of security for your VPC that acts as a firewall for controlling traffic in and out of one or more subnets. Network ACLs with all open ports have a larger attack surface. This search looks for events within your CloudTrail logs to check if there were any Network ACLs created with ports ranging from 1024 to 65525. This search will create a table comprised of AWS account id, src, user and all parameters of the request made by the user and the server response. +action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. action.escu.known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. -action.escu.creation_date = 2017-01-08 +action.escu.creation_date = 2017-01-10 action.escu.modification_date = 2017-01-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Network Access Control List Created with All Open Ports - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Network Access Control List Created with All Open Ports +action.correlationsearch.label = ESCU - AWS Network Access Control List Created with All Open Ports - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = aws_account_id, src, arn -action.notable.param.rule_description = $src_user$ created a network access control list with all ports open. -action.notable.param.rule_title = Network ACL created with all ports open by $src_user$ +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. +action.notable.param.rule_title = AWS Network Access Control List Created with All Open Ports action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = arn -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = arn -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -317,48 +248,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=CreateNetworkAclEntry | mvexpand requestParameters | mvexpand responseElements | search requestParameters.portRange.from=1024 requestParameters.portRange.to=65535 requestParameters.ruleAction=allow | rename userIdentity.arn as arn | rename requestParameters.networkAclId as networkAclId | table _time aws_account_id src userName arn networkAclId requestParameters.* responseElements.* +search = `cloudtrail` eventName=CreateNetworkAclEntry | mvexpand requestParameters | mvexpand responseElements | search requestParameters.portRange.from=1024 requestParameters.portRange.to=65535 requestParameters.ruleAction=allow | rename userIdentity.arn as arn | rename requestParameters.networkAclId as networkAclId | table _time aws_account_id src userName arn networkAclId requestParameters.* responseElements.* | `aws_network_access_control_list_created_with_all_open_ports_filter` [ESCU - AWS Network Access Control List Deleted - Rule] action.escu = 0 action.escu.enabled = 1 description = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. -action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. +action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. -action.escu.creation_date = 2017-01-08 +action.escu.creation_date = 2017-01-10 action.escu.modification_date = 2017-01-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["arn"] -action.escu.entities = ["arn"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Network Access Control List Deleted +action.correlationsearch.label = ESCU - AWS Network Access Control List Deleted - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = arn, eventName -action.notable.param.rule_description = AWS network ACL has been deleted by $arn$. -action.notable.param.rule_title = AWS Network ACL deleted by $arn$ +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. +action.notable.param.rule_title = AWS Network Access Control List Deleted action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = arn -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = arn -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -367,48 +288,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` +search = `cloudtrail` eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `aws_network_access_control_list_deleted_filter` [ESCU - Abnormally High AWS Instances Launched by User - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. action.escu.creation_date = 2018-02-26 action.escu.modification_date = 2018-02-26 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["userName"] -action.escu.entities = ["userName"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */10 * * * * -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Launched by User +action.correlationsearch.label = ESCU - Abnormally High AWS Instances Launched by User - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = userName -action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window -action.notable.param.rule_title = High Number of instances launched by $userName$ +action.notable.param.rule_description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. +action.notable.param.rule_title = Abnormally High AWS Instances Launched by User action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = userName -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = userName -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -417,48 +327,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +search = `cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` [ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances launched by a particular user against the saved baseline data in the model ec2_excessive_runinstances_v1. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. action.escu.creation_date = 2019-11-14 action.escu.modification_date = 2019-11-14 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = */10 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Launched by User - MLTK +action.correlationsearch.label = ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user -action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window -action.notable.param.rule_title = High Number of instances launched by $src_user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. +action.notable.param.rule_title = Abnormally High AWS Instances Launched by User - MLTK action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -467,48 +367,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 +search = `cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 [ESCU - Abnormally High AWS Instances Terminated by User - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. action.escu.known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. action.escu.creation_date = 2018-02-26 action.escu.modification_date = 2018-02-26 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["userName"] -action.escu.entities = ["userName"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] -cron_schedule = */10 * * * * -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Terminated by User +action.correlationsearch.label = ESCU - Abnormally High AWS Instances Terminated by User - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = userName -action.notable.param.rule_description = An abnormally high number of instances were terminated by a user in a 10-minute window -action.notable.param.rule_title = High number of instances terminated by $userName$ +action.notable.param.rule_description = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window +action.notable.param.rule_title = Abnormally High AWS Instances Terminated by User action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = userName -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = userName -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -517,48 +406,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev +search = `cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` [ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances terminated by a particular user against the saved baseline data in the model ec2_excessive_terminateinstances_v1. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. action.escu.creation_date = 2019-11-14 action.escu.modification_date = 2019-11-14 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] -cron_schedule = */10 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Terminated by User - MLTK +action.correlationsearch.label = ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user -action.notable.param.rule_description = An abnormally high number of instances were terminated by a user within in a 10-minute window -action.notable.param.rule_title = High Number of instances terminated by $src_user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. +action.notable.param.rule_title = Abnormally High AWS Instances Terminated by User - MLTK action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -567,16 +446,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 +search = `cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 [ESCU - Access LSASS Memory for Dump Creation - Rule] action.escu = 0 action.escu.enabled = 1 description = Detect memory dumping of the LSASS process. -action.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} -action.escu.eli5 = dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump. +action.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Detect memory dumping of the LSASS process. action.escu.how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. action.escu.creation_date = 2019-12-06 @@ -584,31 +463,21 @@ action.escu.modification_date = 2019-12-06 action.escu.confidence = high action.escu.full_search_name = ESCU - Access LSASS Memory for Dump Creation - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Access LSASS Memory for Dump Creation +action.correlationsearch.label = ESCU - Access LSASS Memory for Dump Creation - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Possible attempt at credential dumping was detected on $dest$. -action.notable.param.rule_title = LSASS memory dump detected on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = Detect memory dumping of the LSASS process. +action.notable.param.rule_title = Access LSASS Memory for Dump Creation action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -617,7 +486,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter` @@ -625,36 +493,30 @@ search = `sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR action.escu = 0 action.escu.enabled = 1 description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API -action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} -action.escu.eli5 = In this search we can detect unauthenticated web requests against an EKS cluster Pod, by looking at k8s authentication data, user agent and source IPs and API direct request. +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides detection information on unauthenticated requests against Kubernetes' Pods API action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -action.escu.creation_date = 2020-03-24 +action.escu.creation_date = 2020-04-15 action.escu.modification_date = 2020-04-15 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Amazon EKS Kubernetes Pod scan detection - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Amazon EKS Kubernetes cluster Pod -action.escu.fields_required = ["cluster_name", "src_ip"] -action.escu.entities = ["cluster_name", "src_ip"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Amazon EKS Kubernetes Pod scan detection +action.correlationsearch.label = ESCU - Amazon EKS Kubernetes Pod scan detection - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user.username, sourceIPs, verb, http_user_agent, group_name, requestURI action.notable.param.rule_description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API action.notable.param.rule_title = Amazon EKS Kubernetes Pod scan detection action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = cluster_name -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high +alert.digest_mode = 1 action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -663,44 +525,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="aws:cloudwatchlogs:eks" "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kubernetes_pods_aws_scan_fingerprint_detection` +search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` [ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] action.escu = 0 action.escu.enabled = 1 description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS -action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} -action.escu.eli5 = In this search we can detect unauthenticated web requests against an EKS cluster, by looking at k8s authentication data, user agent and source IPs. +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -action.escu.creation_date = 2020-03-24 +action.escu.creation_date = 2020-04-15 action.escu.modification_date = 2020-04-15 action.escu.confidence = high action.escu.full_search_name = ESCU - Amazon EKS Kubernetes cluster scan detection - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Amazon EKS Kubernetes cluster -action.escu.fields_required = ["cluster_name", "src_ip"] -action.escu.entities = ["cluster_name", "src_ip"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Amazon EKS Kubernetes cluster scan detection +action.correlationsearch.label = ESCU - Amazon EKS Kubernetes cluster scan detection - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, cluster_name, http_user_agent action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS action.notable.param.rule_title = Amazon EKS Kubernetes cluster scan detection action.notable.param.security_domain = threat action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = cluster_name -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +alert.digest_mode = 1 action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -709,49 +564,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="aws:cloudwatchlogs:eks" "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`kubernetes_aws_scan_fingerprint_detection` +search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` [ESCU - Attempt To Add Certificate To Untrusted Store - Rule] action.escu = 0 action.escu.enabled = 1 description = Attempt to add a certificate to the untrusted certificate store -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. +action.escu.eli5 = Attempt to add a certificate to the untrusted certificate store action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -action.escu.creation_date = 2018-04-09 +action.escu.creation_date = 2018-11-15 action.escu.modification_date = 2018-11-15 action.escu.confidence = high action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store +action.correlationsearch.label = ESCU - Attempt To Add Certificate To Untrusted Store - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = Attempt to add a certificate to the untrusted certificate store -action.notable.param.rule_title = Attempt To Add Certificate to Untrusted Store +action.notable.param.rule_title = Attempt To Add Certificate To Untrusted Store action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -760,49 +604,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` [ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] action.escu = 0 action.escu.enabled = 1 description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1086", "T1064"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for changes of the ExecutionPolicy in the registry. The ExecutionPolicy is a safety feature that controls the conditions under which PowerShell loads configuration files and runs scripts. Usually, the ExecutionPolicy is "Restricted" for Windows clients and "RemoteSigned" for Windows Servers, allowing only certain scripts to run. This search detects when an attacker sets the ExecutionPolicy to "Unrestricted" or "Bypass." +action.escu.eli5 = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. action.escu.known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. -action.escu.creation_date = 2018-08-28 +action.escu.creation_date = 2019-12-02 action.escu.modification_date = 2019-12-02 action.escu.confidence = high action.escu.full_search_name = ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Credential Dumping", "Malicious PowerShell"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Malicious PowerShell", "Credential Dumping"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass +action.correlationsearch.label = ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, registry_path, registry_key_name, registry_value_name -action.notable.param.rule_description = An attempt to modify the default PowerShell execution policy in the registry to "Unrestricted" or "Bypass" was detected on $dest$. -action.notable.param.rule_title = Attempt To Set PowerShell Execution Policy To "Unrestricted" or "Bypass" On $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. +action.notable.param.rule_title = Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -811,7 +644,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter` @@ -819,41 +651,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search looks for attempts to stop security-related services on the endpoint. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. +action.escu.eli5 = This search looks for attempts to stop security-related services on the endpoint. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. -action.escu.creation_date = 2018-04-09 +action.escu.creation_date = 2017-09-15 action.escu.modification_date = 2017-09-15 action.escu.confidence = high action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Stop Security Service +action.correlationsearch.label = ESCU - Attempt To Stop Security Service - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = Attempt to stop a security-related service on $dest$ -action.notable.param.rule_title = Attempt to Stop Security Service On $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for attempts to stop security-related services on the endpoint. +action.notable.param.rule_title = Attempt To Stop Security Service action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -862,49 +684,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` -[ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule] +[ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] action.escu = 0 action.escu.enabled = 1 description = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. +action.escu.eli5 = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified. -action.escu.creation_date = 2018-08-28 +action.escu.creation_date = 2019-12-02 action.escu.modification_date = 2019-12-02 action.escu.confidence = high -action.escu.full_search_name = ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule +action.escu.full_search_name = ESCU - Attempted Credential Dump From Registry via Reg exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempted Credential Dump From Registry via Reg.exe +action.correlationsearch.label = ESCU - Attempted Credential Dump From Registry via Reg exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, process_name, process -action.notable.param.rule_description = An attempt to save registry keys holding credentials was identified by $user$. -action.notable.param.rule_title = Attempted Credential Dump From Registry from $user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. +action.notable.param.rule_title = Attempted Credential Dump From Registry via Reg exe action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -913,17 +724,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter` [ESCU - Batch File Write to System32 - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for a batch file (.bat) written to the Windows system directory tree. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. +action.escu.eli5 = The search looks for a batch file (.bat) written to the Windows system directory tree. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. action.escu.creation_date = 2018-12-14 @@ -931,31 +741,21 @@ action.escu.modification_date = 2018-12-14 action.escu.confidence = high action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Batch File Write to System32 +action.correlationsearch.label = ESCU - Batch File Write to System32 - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A batch file was written to the system directory on $dest$. -action.notable.param.rule_title = Batch file write to system32 detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for a batch file (.bat) written to the Windows system directory tree. +action.notable.param.rule_title = Batch File Write to System32 action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -964,49 +764,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` -[ESCU - Child Processes of Spoolsv.exe - Rule] +[ESCU - Child Processes of Spoolsv exe - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. -action.escu.mappings = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Privilege Escalation", "Exploitation for Privilege Escalation"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for child processes of spoolsv.exe, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f. +action.escu.eli5 = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. action.escu.known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. -action.escu.creation_date = 2018-11-26 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Child Processes of Spoolsv.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Child Processes of Spoolsv exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Privilege Escalation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Child Processes of Spoolsv.exe +action.correlationsearch.label = ESCU - Child Processes of Spoolsv exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, parent_process_name -action.notable.param.rule_description = A child process of spoolsv.exe was detected on $dest$. -action.notable.param.rule_title = Spoolsv.exe spawned a child process on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. +action.notable.param.rule_title = Child Processes of Spoolsv exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, parent_process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1015,51 +804,40 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `children_of_spoolsv_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter` [ESCU - Clients Connecting to Multiple DNS Servers - Rule] action.escu = 0 action.escu.enabled = 1 description = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the `Network_Resolution` data model. +action.escu.eli5 = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. action.escu.how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-16 action.escu.modification_date = 2020-01-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Clients Connecting to Multiple DNS Servers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "src"] -action.escu.entities = ["dest", "src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "DNS Hijacking", "Host Redirection", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Clients Connecting to Multiple DNS Servers +action.correlationsearch.label = ESCU - Clients Connecting to Multiple DNS Servers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, dest -action.notable.param.rule_description = This search allows you to identify the endpoints that have connected to more than five DNS servers over the time frame specified in the search. -action.notable.param.rule_title = Client $src$ Connecting to Multiple DNS Servers +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. +action.notable.param.rule_title = Clients Connecting to Multiple DNS Servers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1068,9 +846,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_output_filter` +search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` [ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] action.escu = 0 @@ -1078,41 +855,29 @@ action.escu.enabled = 1 description = This search looks for cloud compute instances created by users who have not created them before. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = For each user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each user back to the lookup file and then clears out any output. The other part of the search limits the results to when the user was seen for the first time within the previous 70 minutes. It then displays the new user, the instances created by that user, and the associated times. +action.escu.eli5 = This search looks for cloud compute instances created by users who have not created them before. action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. action.escu.known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -action.escu.creation_date = 2019-10-03 +action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Cloud Compute Instance -action.escu.fields_required = ["dest", "src_user"] -action.escu.entities = ["dest", "src_user"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Cloud Compute Instance Created By Previously Unseen User +action.correlationsearch.label = ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user, dest -action.notable.param.rule_description = The compute instance $dest$ was created by $src_user$. This is the first time $src_user$ has created a compute instance. -action.notable.param.rule_title = Cloud Compute Instance Created By Previously Unseen User $src_user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for cloud compute instances created by users who have not created them before. +action.notable.param.rule_title = Cloud Compute Instance Created By Previously Unseen User action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.drilldown_name = Show all instances created by $src_user$ -action.notable.param.drilldown_search = | from datamodel:Cloud_Infrastructure.Compute | action=run src_user=$src_user$ -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user, dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1121,9 +886,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run by Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by src_user | multireport [| table src_user, firstTime, lastTime | outputlookup previously_seen_cloud_compute_creations_by_user | where fact=fiction][| eval new_user=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_creations_by_user_search_window_begin_offset`), 1, 0) | where new_user=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table src_user, dest, firstTime, lastTime +search = | tstats `security_content_summariesonly` earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run by Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by src_user | multireport [| table src_user, firstTime, lastTime | outputlookup previously_seen_cloud_compute_creations_by_user | where fact=fiction][| eval new_user=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_creations_by_user_search_window_begin_offset`), 1, 0) | where new_user=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table src_user, dest, firstTime, lastTime | `cloud_compute_instance_created_by_previously_unseen_user_filter` [ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] action.escu = 0 @@ -1131,41 +895,29 @@ action.escu.enabled = 1 description = This search looks for cloud compute instances being created with previously unseen image IDs. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = For each image ID and user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each image back to the lookup file and clears out any output. The other part of the search limits the results to when the image was seen for the first time within the previous 70 minutes. It then displays the new image, the instances created using it, the user who created it, and the associated times. +action.escu.eli5 = This search looks for cloud compute instances being created with previously unseen image IDs. action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images. action.escu.known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. -action.escu.creation_date = 2019-10-03 +action.escu.creation_date = 2018-10-12 action.escu.modification_date = 2018-10-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Cloud Compute Instance -action.escu.fields_required = ["dest", "src_user"] -action.escu.entities = ["dest", "src_user"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Cloud Compute Instance Created With Previously Unseen Image +action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The cloud compute instance $dest$ was created with previously unused image $image_id$. -action.notable.param.rule_title = Cloud Compute Instances Created With New Image +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for cloud compute instances being created with previously unseen image IDs. +action.notable.param.rule_title = Cloud Compute Instance Created With Previously Unseen Image action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.drilldown_name = Show all instances created with image $image_id$ -action.notable.param.drilldown_search = | from datamodel:Cloud_Infrastructure.Compute | image_id=$image_id$ -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1174,9 +926,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` by Compute.image_id, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by image_id, src_user | multireport [| table image_id, firstTime, lastTime | outputlookup previously_seen_cloud_compute_images | where fact=fiction][| eval new_image=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_image_search_window_begin_offset`), 1, 0) | where new_image=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table image_id, dest, src_user, firstTime, lastTime +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `cloud_compute_instance_created_with_previously_unseen_image_filter` by Compute.image_id, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by image_id, src_user | multireport [| table image_id, firstTime, lastTime | outputlookup previously_seen_cloud_compute_images | where fact=fiction][| eval new_image=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_image_search_window_begin_offset`), 1, 0) | where new_image=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table image_id, dest, src_user, firstTime, lastTime [ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] action.escu = 0 @@ -1184,41 +935,29 @@ action.escu.enabled = 1 description = Find EC2 instances being created with previously unseen instance types. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = For each instance type and user, the search returns the first time seen, last time seen, and the system. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each instance type back to the lookup file and clears out any output. The other part of the search limits the results to when the instance type was seen for the first time within the previous 70 minutes. It then displays the new instance type, the instances created using it, the user who created them, and the times associated. +action.escu.eli5 = Find EC2 instances being created with previously unseen instance types. action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. -action.escu.creation_date = 2019-10-03 +action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Cloud Compute Instance -action.escu.fields_required = ["dest", "src_user"] -action.escu.entities = ["dest", "src_user"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Cloud Compute Instance Created With Previously Unseen Instance Type +action.correlationsearch.label = ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = instance_type -action.notable.param.rule_description = The instance type $instance_type$ was used for the first time to create $dest$. -action.notable.param.rule_title = New Cloud Compute Instance Type $instance_type$ detected +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Find EC2 instances being created with previously unseen instance types. +action.notable.param.rule_title = Cloud Compute Instance Created With Previously Unseen Instance Type action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.drilldown_name = Find all compute instances of type $instance_type$ -action.notable.param.drilldown_search = | from datamodel:Cloud_Infrastructure.Compute | instance_type=$instance_type$ -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1227,51 +966,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances `previously_seen_cloud_compute_instance_types_input_filter` by Compute.instance_type, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_instance_types | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by instance_type, src_user | multireport [| table instance_type, firstTime, lastTime | outputlookup previously_seen_cloud_compute_instance_types | where fact=fiction][| eval new_type=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_instance_types_search_window_begin_offset`), 1, 0) | where new_type=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table instance_type, dest, src_user, firstTime, lastTime +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` by Compute.instance_type, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_compute_instance_types | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by instance_type, src_user | multireport [| table instance_type, firstTime, lastTime | outputlookup previously_seen_cloud_compute_instance_types | where fact=fiction][| eval new_type=if(firstTime >= relative_time(now(), `previously_seen_cloud_compute_instance_types_search_window_begin_offset`), 1, 0) | where new_type=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table instance_type, dest, src_user, firstTime, lastTime [ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. -action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = In this search, we query cloud infrastructure compute logs to look for events that indicate that an instance was started in a particular region. Using the \"previously_seen_cloud_regions\" lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The \"eval\" and \"if\" functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with \"Instance Started in a New Region.\" However, this region will be added to the list in \"previously_seen_cloud_regions.\" +action.escu.eli5 = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions. action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. action.escu.creation_date = 2019-10-02 action.escu.modification_date = 2019-10-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Cloud Compute Instance -action.escu.fields_required = ["dest", "region", "src_user"] -action.escu.entities = ["dest", "region", "src_user"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Cloud Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Cloud Compute Instance Started In Previously Unused Region +action.correlationsearch.label = ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest,region -action.notable.param.rule_description = A cloud compute instance, $dest$, is started in a new, previously unseen, region $region$ -action.notable.param.rule_title = Cloud instance $dest$ started in a new region $region$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. +action.notable.param.rule_title = Cloud Compute Instance Started In Previously Unused Region action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.drilldown_name = See all activity in $region$ -action.notable.param.drilldown_search = | from datamodel:Cloud_Infrastructure.Compute | search region="$region$" -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,region -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1280,53 +1006,42 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances `previously_seen_cloud_regions_input_filter` by Compute.region, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_regions | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by region, src_user | multireport [| table region, firstTime, lastTime | outputlookup previously_seen_cloud_regions | where fact=fiction][| eval new_region=if(firstTime >= relative_time(now(), `previously_seen_cloud_regions_search_window_begin_offset`), 1, 0) | where new_region=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table region, dest, src_user, firstTime, lastTime +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(Compute.dest) as dest from datamodel=Cloud_Infrastructure.Compute where Compute.event_name=RunInstances `cloud_compute_instance_started_in_previously_unused_region_filter` by Compute.region, Compute.src_user | `drop_dm_object_name("Compute")` | inputlookup append=t previously_seen_cloud_regions | stats min(firstTime) as firstTime max(lastTime) as lastTime, values(dest) as dest by region, src_user | multireport [| table region, firstTime, lastTime | outputlookup previously_seen_cloud_regions | where fact=fiction][| eval new_region=if(firstTime >= relative_time(now(), `previously_seen_cloud_regions_search_window_begin_offset`), 1, 0) | where new_region=1 | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`] | table region, dest, src_user, firstTime, lastTime [ESCU - Common Ransomware Extensions - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for file modifications with extensions commonly used by Ransomware -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. +action.escu.eli5 = The search looks for file modifications with extensions commonly used by Ransomware action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -action.escu.creation_date = 2017-08-21 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Common Ransomware Extensions +action.correlationsearch.label = ESCU - Common Ransomware Extensions - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification was detected on $dest$ with an extension commonly used by ransomware. -action.notable.param.rule_title = Ransomware Extension detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for file modifications with extensions commonly used by Ransomware +action.notable.param.rule_title = Common Ransomware Extensions action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1335,7 +1050,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` @@ -1343,41 +1057,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at file modifications in the Change Analysis data model. It checks modified file names against an included lookup file, which contains the names of note files left behind by ransomware (to inform the victim how they can pay the ransom and retrieve their files). The search returns a list of files with matching names. +action.escu.eli5 = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. action.escu.how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. action.escu.known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. -action.escu.creation_date = 2017-08-21 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high action.escu.full_search_name = ESCU - Common Ransomware Notes - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Common Ransomware Notes +action.correlationsearch.label = ESCU - Common Ransomware Notes - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification associated with a ransomware victim notification file detected on $dest$ -action.notable.param.rule_title = Ransomware Note File detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. +action.notable.param.rule_title = Common Ransomware Notes action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1386,16 +1090,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`|`ransomware_notes`| `common_ransomware_notes_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter` [ESCU - Create Remote Thread into LSASS - Rule] action.escu = 0 action.escu.enabled = 1 description = Detect remote thread creation into LSASS consistent with credential dumping. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} -action.escu.eli5 = This search detects the creation of a remote thread into LSASS (Local Security Authority Subsystem Service). This technique can be used by attackers to inject code into LSASS and dump the memory in order to obtain credentials. +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Detect remote thread creation into LSASS consistent with credential dumping. action.escu.how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. action.escu.creation_date = 2019-12-06 @@ -1403,31 +1107,21 @@ action.escu.modification_date = 2019-12-06 action.escu.confidence = high action.escu.full_search_name = ESCU - Create Remote Thread into LSASS - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Create Remote Thread into LSASS +action.correlationsearch.label = ESCU - Create Remote Thread into LSASS - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Possible attempt at credential dumping was detected on $dest$. -action.notable.param.rule_title = remote thread creation into LSASS on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = Detect remote thread creation into LSASS consistent with credential dumping. +action.notable.param.rule_title = Create Remote Thread into LSASS action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, TargetProcessId -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1436,49 +1130,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = `sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_remote_thread_into_lsass_filter` +search = `sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter` -[ESCU - Create local admin accounts using net.exe - Rule] +[ESCU - Create local admin accounts using net exe - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the creation of local administrator accounts using net.exe. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as `localgroup`, `add`, or `user` that may correspond to the creation of local admin accounts or setting user/group properties. +action.escu.eli5 = This search looks for the creation of local administrator accounts using net.exe. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators often leverage net.exe to create admin accounts. -action.escu.creation_date = 2018-03-28 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Create local admin accounts using net.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Create local admin accounts using net exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = 0 8 * * * -dispatch.earliest_time = -1440m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Create local admin accounts using net.exe +action.correlationsearch.label = ESCU - Create local admin accounts using net exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Net.exe was used to create local administrator accounts on $dest$. -action.notable.param.rule_title = Local administrator account created on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the creation of local administrator accounts using net.exe. +action.notable.param.rule_title = Create local admin accounts using net exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1487,49 +1170,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search (process=*localgroup* OR process=*/add* OR process=*user*) |`create_local_admin_via_net_filter` +search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`create_local_admin_accounts_using_net_exe_filter` -[ESCU - Create or delete windows shares using net.exe - Rule] +[ESCU - Create or delete windows shares using net exe - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the creation or deletion of hidden shares using net.exe. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement"], "mitre_technique_id": ["T1077", "T1126"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation/deletion of windows drive shares. Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete (hidden) file shares by appending "$" after the name of the share. Since the creation/deletion of hidden shares is a special case of detecting share creation/deletion we have commented out the regex that adds that additional matching criteria. If only hidden share detection is desired add `| regex process="\S+[$]"` before the last pipe in the search. +action.escu.eli5 = This search looks for the creation or deletion of hidden shares using net.exe. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -action.escu.creation_date = 2018-06-14 +action.escu.creation_date = 2020-01-20 action.escu.modification_date = 2020-01-20 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Create or delete windows shares using net.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Create or delete windows shares using net exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Create or delete windows shares using net.exe +action.correlationsearch.label = ESCU - Create or delete windows shares using net exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest,process_name -action.notable.param.rule_description = Net.exe was used to create or delete hidden network shares by $user$ on $dest$ -action.notable.param.rule_title = Hidden File shares created/deleted on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the creation or deletion of hidden shares using net.exe. +action.notable.param.rule_title = Create or delete windows shares using net exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1538,17 +1210,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search (process=*share* AND process=*delete*) | `create_or_delete_windows_shares_filter` +search = | tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter` [ESCU - Creation of Shadow Copy - Rule] action.escu = 0 action.escu.enabled = 1 description = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers will first create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using Ntdsutil, Vssadmin, or Wmic. +action.escu.eli5 = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. action.escu.creation_date = 2019-12-10 @@ -1556,31 +1227,21 @@ action.escu.modification_date = 2019-12-10 action.escu.confidence = high action.escu.full_search_name = ESCU - Creation of Shadow Copy - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Creation of Shadow Copy +action.correlationsearch.label = ESCU - Creation of Shadow Copy - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest, process_name, process -action.notable.param.rule_description = An attempt to create a shadow copy with Ntdsutil, Vssadmin, or Wmic was detected on $dest$. -action.notable.param.rule_title = Attempted Credential Dump of ntds.dit from $user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. +action.notable.param.rule_title = Creation of Shadow Copy action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1589,7 +1250,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) OR (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter` @@ -1597,40 +1257,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search detects the use of wmic and Powershell to create a shadow copy. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} -action.escu.eli5 = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using wmic, which is executed by Powershell. +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects the use of wmic and Powershell to create a shadow copy. action.escu.how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = Legtimate administrator usage of wmic to create a shadow copy. action.escu.creation_date = 2019-12-10 action.escu.modification_date = 2019-12-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Creation of Shadow Copy with wmic and powershell - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Creation of Shadow Copy with wmic and powershell +action.correlationsearch.label = ESCU - Creation of Shadow Copy with wmic and powershell - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = An attempt to create a shadow copy with wmic and Powershell was detected on $dest$. -action.notable.param.rule_title = Attempted Credential Dump of ntds.dit on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search detects the use of wmic and Powershell to create a shadow copy. +action.notable.param.rule_title = Creation of Shadow Copy with wmic and powershell action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1639,7 +1290,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = `sysmon` Message=*win32_shadowcopy* Message=*Create* | stats count min(_time) as firstTime max(_time) as lastTime by dvc User EventCode Message | rename User as user, dvc as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter` @@ -1647,9 +1297,9 @@ search = `sysmon` Message=*win32_shadowcopy* Message=*Create* | stats count min( action.escu = 0 action.escu.enabled = 1 description = This search detects credential dumping using copy command from a shadow copy. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The file system, security, sam and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied. But it is possible by creating first a shadow copy and then copy it from the shadow copy. This search will detect this attack of credential dumping. +action.escu.eli5 = This search detects credential dumping using copy command from a shadow copy. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2019-12-10 @@ -1657,31 +1307,21 @@ action.escu.modification_date = 2019-12-10 action.escu.confidence = high action.escu.full_search_name = ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Credential Dumping via Copy Command from Shadow Copy +action.correlationsearch.label = ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest, process_name, process -action.notable.param.rule_description = credential dumping using copy command was detected on $dest$. -action.notable.param.rule_title = Attempted Credential Dump using copy command from $user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search detects credential dumping using copy command from a shadow copy. +action.notable.param.rule_title = Credential Dumping via Copy Command from Shadow Copy action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1690,17 +1330,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `cred_dump_via_copy_from_shadowcopy_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter` [ESCU - Credential Dumping via Symlink to Shadow Copy - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects the creation of a symlink to a shadow copy. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The file system, security, sam, and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied, but it can be done by creating shadow copy and then create a symlink to the shadow copy. This search will detect this attack of credential dumping. +action.escu.eli5 = This search detects the creation of a symlink to a shadow copy. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2019-12-10 @@ -1708,31 +1347,21 @@ action.escu.modification_date = 2019-12-10 action.escu.confidence = high action.escu.full_search_name = ESCU - Credential Dumping via Symlink to Shadow Copy - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Credential Dumping via Symlink to Shadow Copy +action.correlationsearch.label = ESCU - Credential Dumping via Symlink to Shadow Copy - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest, process_name, process -action.notable.param.rule_description = credential dumping using symlink on $dest$. -action.notable.param.rule_title = Attempted Credential Dump using symlink from $user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search detects the creation of a symlink to a shadow copy. +action.notable.param.rule_title = Credential Dumping via Symlink to Shadow Copy action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1741,17 +1370,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `cred_dump_via_symlink_shadowcopy_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter` [ESCU - DNS Query Length Outliers - MLTK - Rule] action.escu = 0 action.escu.enabled = 1 description = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search "Baseline of DNS Query Length - MLTK" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.eli5 = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -1760,36 +1388,26 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. -action.escu.creation_date = 2019-05-08 +action.escu.creation_date = 2020-01-22 action.escu.modification_date = 2020-01-22 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - DNS Query Length Outliers - MLTK - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS Query Length Outliers - MLTK +action.correlationsearch.label = ESCU - DNS Query Length Outliers - MLTK - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = Identify DNS traffic with unusual query lengths by record type -action.notable.param.rule_title = DNS query length outliers +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. +action.notable.param.rule_title = DNS Query Length Outliers - MLTK action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = query -alert.suppress.period = 43200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1798,49 +1416,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_mltk_filter` +search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` [ESCU - DNS Query Length With High Standard Deviation - Rule] action.escu = 0 action.escu.enabled = 1 description = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = Attackers often use random, long domain names for their attack infrastructure. This search looks at all the queries observed over the search time frame, and identifies any domains being resolved with names that are greater that 2 times the standard deviation. +action.escu.eli5 = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. action.escu.known_false_positives = It's possible there can be long domain names that are legitimate. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-22 action.escu.modification_date = 2020-01-22 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - DNS Query Length With High Standard Deviation - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS Query Length With High Standard Deviation +action.correlationsearch.label = ESCU - DNS Query Length With High Standard Deviation - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = Filter DNS requests and compute the standard deviation then filter on 2 times the standard deviation -action.notable.param.rule_title = DNS query length with high standard deviation +action.notable.param.rule_description = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. +action.notable.param.rule_title = DNS Query Length With High Standard Deviation action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = query -alert.suppress.period = 43200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1849,7 +1455,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` @@ -1857,41 +1462,31 @@ search = | tstats `security_content_summariesonly` count from datamodel=Network_ action.escu = 0 action.escu.enabled = 1 description = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. -action.escu.mappings = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. +action.escu.eli5 = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. -action.escu.creation_date = 2017-07-08 +action.escu.creation_date = 2020-01-17 action.escu.modification_date = 2020-01-17 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "src"] -action.escu.entities = ["dest", "src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "DNS Hijacking", "Host Redirection", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers +action.correlationsearch.label = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = The table represents a list of unauthorized DNS servers interacting with hosts in your network -action.notable.param.rule_title = DNS requests resolved by unauthorized DNS servers +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. +action.notable.param.rule_title = DNS Query Requests Resolved by Unauthorized DNS Servers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1900,53 +1495,42 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `unauthorized_dns_servers_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` [ESCU - DNS record changed - Rule] action.escu = 0 action.escu.enabled = 1 description = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. -action.escu.mappings = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. +action.escu.eli5 = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ action.escu.known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. -action.escu.creation_date = 2019-02-14 +action.escu.creation_date = 2020-01-16 action.escu.modification_date = 2020-01-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - DNS record changed - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src", "dest"] -action.escu.entities = ["src", "dest"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DNS Hijacking"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS record changed +action.correlationsearch.label = ESCU - DNS record changed - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = The table represents a list of DNS records and their responses for corporate domains that have recently changed +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. action.notable.param.rule_title = DNS record changed action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -1955,7 +1539,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` @@ -1963,41 +1546,31 @@ search = | inputlookup discovered_dns_records.csv | rename answer as discovered_ action.escu = 0 action.escu.enabled = 1 description = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Impact"], "mitre_technique_id": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for execution of vssadmin or wmic with both the "delete" and "shadows" parameters passed on the command-line. The two arguments are searched for separately because we can't predict the number of spaces between the words on the command-line. The search will return the number of times this activity was observed, and the times of the first and last event. +action.escu.eli5 = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. -action.escu.creation_date = 2017-02-17 +action.escu.creation_date = 2020-04-17 action.escu.modification_date = 2020-04-17 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Deleting Shadow Copies - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware", "Windows Log Manipulation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Deleting Shadow Copies +action.correlationsearch.label = ESCU - Deleting Shadow Copies - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = Using $process_name$ to delete shadow copies is common behavior by ransomware. This activity was observed on $dest$ -action.notable.param.rule_title = Deleting Shadow Copies on $dest$ with $process_name$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. +action.notable.param.rule_title = Deleting Shadow Copies action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2006,16 +1579,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*delete* AND process=*shadow* +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*delete* AND process=*shadow* | `deleting_shadow_copies_filter` [ESCU - Detect API activity from users without MFA - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "PR.AC"]} -action.escu.eli5 = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of the user. +action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -2026,34 +1599,24 @@ Detailed documentation on how to create a new field within Incident Review may b action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. action.escu.creation_date = 2018-05-17 action.escu.modification_date = 2018-05-17 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect API activity from users without MFA - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = 0 8 * * * -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect API activity from users without MFA +action.correlationsearch.label = ESCU - Detect API activity from users without MFA - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = API Activity detected from $user$ without MFA enabled. -action.notable.param.rule_title = API Activity detected from $user$ without MFA enabled +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. +action.notable.param.rule_title = Detect API activity from users without MFA action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 84600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2062,16 +1625,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = `cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter` [ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} -action.escu.eli5 = In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the `identity_lookup_expanded` lookup file and the service accounts. Once filtered out, we output a table with the event names and count, as well as the first and last time a specific user or service is detected. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -2080,36 +1643,26 @@ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not ye 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. -action.escu.creation_date = 2018-03-12 +action.escu.creation_date = 2018-03-13 action.escu.modification_date = 2018-03-13 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect AWS API Activities From Unapproved Accounts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect AWS API Activities From Unapproved Accounts +action.correlationsearch.label = ESCU - Detect AWS API Activities From Unapproved Accounts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A successful API activity was invoked by $user$, an unapproved/unknown account. -action.notable.param.rule_title = Successful API activity by a non-approved account: $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. +action.notable.param.rule_title = Detect AWS API Activities From Unapproved Accounts action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2118,48 +1671,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = `cloudtrail` errorCode=success | rename userName as identity | search NOT [| inputlookup identity_lookup_expanded | fields identity] | search NOT [| inputlookup aws_service_accounts | fields identity] | rename identity as user | stats count min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_api_activities_from_unapproved_accounts_filter` [ESCU - Detect AWS Console Login by User from New City - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and city combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -action.escu.creation_date = 2018-04-24 +action.escu.creation_date = 2018-04-30 action.escu.modification_date = 2018-04-30 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New City - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect AWS Console Login by User from New City +action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New City - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A user has logged into the AWS console from a new city. -action.notable.param.rule_title = AWS Console Login by User from New City +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour +action.notable.param.rule_title = Detect AWS Console Login by User from New City action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2168,48 +1711,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user City | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New City","Previously Seen City") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New City" AND UserData="Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `security_content_ctime(earliestseen)` | table user City userStatus firstTime lastTime earliestseen +search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user City | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New City","Previously Seen City") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New City" AND UserData="Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `security_content_ctime(earliestseen)` | table user City userStatus firstTime lastTime earliestseen | `detect_aws_console_login_by_user_from_new_city_filter` [ESCU - Detect AWS Console Login by User from New Country - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and country combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -action.escu.creation_date = 2018-04-24 +action.escu.creation_date = 2018-04-30 action.escu.modification_date = 2018-04-30 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New Country - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect AWS Console Login by User from New Country +action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Country - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A user has logged into the AWS console from a new country. -action.notable.param.rule_title = AWS Console Login by User from New Country +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour +action.notable.param.rule_title = Detect AWS Console Login by User from New Country action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2218,48 +1751,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user Country | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New Country","Previously Seen Country") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New Country" AND UserData="Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`security_content_ctime(earliestseen)` | table user Country userStatus firstTime lastTime earliestseen +search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user Country | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New Country","Previously Seen Country") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New Country" AND UserData="Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`security_content_ctime(earliestseen)` | table user Country userStatus firstTime lastTime earliestseen | `detect_aws_console_login_by_user_from_new_country_filter` [ESCU - Detect AWS Console Login by User from New Region - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and region combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -action.escu.creation_date = 2018-04-24 +action.escu.creation_date = 2018-04-30 action.escu.modification_date = 2018-04-30 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect AWS Console Login by User from New Region - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect AWS Console Login by User from New Region +action.correlationsearch.label = ESCU - Detect AWS Console Login by User from New Region - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A user has logged into the AWS console from a new region. -action.notable.param.rule_title = AWS Console Login by User from New Region +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour +action.notable.param.rule_title = Detect AWS Console Login by User from New Region action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2268,48 +1791,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user Region | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New Region","Previously Seen Region") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New Region" AND UserData="Old User" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `security_content_ctime(earliestseen)` | table user Region userStatus firstTime lastTime earliestseen +search = | inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user Region | join user type=outer [| inputlookup previously_seen_users_console_logins.csv | stats min(firstTime) AS earliestseen by user | fields earliestseen user] | eval userStatus=if(firstTime >= relative_time(now(), "@d"), "New Region","Previously Seen Region") | eval UserData=if(earliestseen >= relative_time(now(), "@d") OR isnull(earliestseen), "New User","Old User") | where userStatus="New Region" AND UserData="Old User" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `security_content_ctime(earliestseen)` | table user Region userStatus firstTime lastTime earliestseen | `detect_aws_console_login_by_user_from_new_region_filter` [ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Pass the Hash"], "mitre_technique_id": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.eli5 = To detect pass the hash activity, we look at all events with event code 4624 that specify a logon type 3 (network logons) for remote pass the hash attacks and logon type 9 for local pass the hash attacks. The search also filters out events with an account name of 'Anonymous' to help reduce false positives. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. action.escu.how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. action.escu.known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-09 action.escu.modification_date = 2020-01-09 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Lateral Movement"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Activity Related to Pass the Hash Attacks +action.correlationsearch.label = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest, user -action.notable.param.rule_description = This search looks for Authentication log events from the Windows Security Audit logs to detect potential attempts for Passing the Hash -action.notable.param.rule_title = Detect Activity Related to Pass the Hash +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. +action.notable.param.rule_title = Detect Activity Related to Pass the Hash Attacks action.notable.param.security_domain = access action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2318,7 +1831,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = `wineventlog_security` EventCode=4624 (Logon_Type=3 LogonProcessName=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (EventCode=4624 Logon_Type=9 LogonProcessName=seclogo) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` @@ -2326,40 +1838,31 @@ search = `wineventlog_security` EventCode=4624 (Logon_Type=3 LogonProcessName=Nt action.escu = 0 action.escu.enabled = 1 description = This search looks for reading lsass memory consistent with credential dumping. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -action.escu.eli5 = This search looks for LSASS access using Credential Dumping tools by detecting Process access with Sysmon logs (EventCode 10), TargetImage lsass.exe and GrantedAccess 0x1410 or 0x1010. This will for example detect the use of sekurlsa::logonpasswords in Mimikatz. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for reading lsass memory consistent with credential dumping. action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -action.escu.creation_date = 2018-08-28 +action.escu.creation_date = 2019-12-03 action.escu.modification_date = 2019-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Credential Dumping through LSASS access - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Credential Dumping through LSASS access +action.correlationsearch.label = ESCU - Detect Credential Dumping through LSASS access - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = Possible attempt at credential dumping was detected on $dest$. -action.notable.param.rule_title = Detect reading lsass memory on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for reading lsass memory consistent with credential dumping. +action.notable.param.rule_title = Detect Credential Dumping through LSASS access action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest, ProcessName -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2368,17 +1871,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = `sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_LSASS_access_filter` +search = `sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter` [ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Spearphishing Link", "Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} -action.escu.data_models = ["Network_Resolution", "Web"] -action.escu.eli5 = This search gathers all the answers to each system's DNS query, then filters for queries that have subdomains extracted from the EvilGinx toolkit. It will then run a regex to extract `legit_domains` from the query and remove that from the detection if it is listed in the `legit_domains.csv` +action.escu.mappings = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.data_models = ["Network_Resolution"] +action.escu.eli5 = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. action.escu.how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ @@ -2390,31 +1892,21 @@ action.escu.modification_date = 2019-04-29 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Common Phishing Frameworks"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect DNS requests to Phishing Sites leveraging EvilGinx2 +action.correlationsearch.label = ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = The host $src$ issued a DNS request for a domain that could be a phishing site leverating EvilGinx toolkit. -action.notable.param.rule_title = DNS request for EvilGinx subdomain detected on $src$ +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. +action.notable.param.rule_title = Detect DNS requests to Phishing Sites leveraging EvilGinx2 action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src, query -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2423,53 +1915,42 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` [ESCU - Detect Excessive Account Lockouts From Endpoint - Rule] action.escu = 0 action.escu.enabled = 1 description = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} action.escu.data_models = ["Change"] -action.escu.eli5 = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five. +action.escu.eli5 = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. action.escu.how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ action.escu.known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. -action.escu.creation_date = 2017-08-17 +action.escu.creation_date = 2019-04-18 action.escu.modification_date = 2019-04-18 -action.escu.confidence = low +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Excessive Account Lockouts From Endpoint - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -4h@h -dispatch.latest_time = -5m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Excessive Account Lockouts From Endpoint +action.correlationsearch.label = ESCU - Detect Excessive Account Lockouts From Endpoint - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ has generated a high number of account lockouts. -action.notable.param.rule_title = $dest$ has generated a high number of account lockouts +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. +action.notable.param.rule_title = Detect Excessive Account Lockouts From Endpoint action.notable.param.security_domain = access -action.notable.param.severity = low -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2478,49 +1959,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` [ESCU - Detect Excessive User Account Lockouts - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects user accounts that have been locked out a relatively high number of times in a short period. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} action.escu.data_models = ["Change"] -action.escu.eli5 = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times a user has caused an account lockout within a four hour window and displays those users with a count greater than or equal to five. +action.escu.eli5 = This search detects user accounts that have been locked out a relatively high number of times in a short period. action.escu.how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. action.escu.known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. -action.escu.creation_date = 2017-08-17 +action.escu.creation_date = 2019-03-01 action.escu.modification_date = 2019-03-01 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Excessive User Account Lockouts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -4h@h -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Excessive User Account Lockouts +action.correlationsearch.label = ESCU - Detect Excessive User Account Lockouts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = The account $user$ has been locked out an excessive number of times -action.notable.param.rule_title = $user$ locked account an excessive number of times +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search detects user accounts that have been locked out a relatively high number of times in a short period. +action.notable.param.rule_title = Detect Excessive User Account Lockouts action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2529,49 +1999,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` [ESCU - Detect Large Outbound ICMP Packets - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "nist": ["DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization. +action.escu.eli5 = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model action.escu.known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary. action.escu.creation_date = 2018-06-01 action.escu.modification_date = 2018-06-01 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Large Outbound ICMP Packets - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Command and Control"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Large Outbound ICMP Packets +action.correlationsearch.label = ESCU - Detect Large Outbound ICMP Packets - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = Large outbound ICMP packet detected. -action.notable.param.rule_title = Large ICMP packet from $src_ip$ to $dest_ip$ detected +action.notable.param.rule_description = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. +action.notable.param.rule_title = Detect Large Outbound ICMP Packets action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2580,49 +2038,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` [ESCU - Detect Long DNS TXT Record Response - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. +action.escu.eli5 = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. action.escu.how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -action.escu.creation_date = 2017-06-18 +action.escu.creation_date = 2017-09-18 action.escu.modification_date = 2017-09-18 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Long DNS TXT Record Response +action.correlationsearch.label = ESCU - Detect Long DNS TXT Record Response - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = A DNS TXT record response of over 100 characters was detected. -action.notable.param.rule_title = Long DNS TXT Record Response +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. +action.notable.param.rule_title = Detect Long DNS TXT Record Response action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2631,16 +2078,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` [ESCU - Detect Mimikatz Using Loaded Images - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for reading loaded Images unique to credential dumping with Mimikatz. -action.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.AE", "DE.CM"]} -action.escu.eli5 = This search looks for loaded images (dll) unique for Mimikatz using Sysmon EventCode 7 logs. +action.escu.mappings = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.AE", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for reading loaded Images unique to credential dumping with Mimikatz. action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist. action.escu.creation_date = 2019-12-03 @@ -2648,31 +2095,21 @@ action.escu.modification_date = 2019-12-03 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Mimikatz Using Loaded Images - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Mimikatz Using Loaded Images +action.correlationsearch.label = ESCU - Detect Mimikatz Using Loaded Images - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Possible attempt at credential dumping was detected on $dest$. -action.notable.param.rule_title = Detect Mimikatz using loaded images on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for reading loaded Images unique to credential dumping with Mimikatz. +action.notable.param.rule_title = Detect Mimikatz Using Loaded Images action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, Image -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2681,7 +2118,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = `sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter` @@ -2689,40 +2125,30 @@ search = `sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values( action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell requesting privileges consistent with credential dumping. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -action.escu.eli5 = This search looks for Windows Event Code(signature_id) 4703 (token right adjusted), where the process requesting the token change is PowerShell.exe and the requested privilege is "SeDebugPrivilege". This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for PowerShell requesting privileges consistent with credential dumping. action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -action.escu.creation_date = 2018-08-28 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = [] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4703 +action.correlationsearch.label = ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = Possible attempt at credential dumping via PowerShell was detected on $dest$ by $user$. -action.notable.param.rule_title = Event Code 4703 Specifying PowerShell Acquiring A Token with SeDebugPrivilege Identified on $dest$. +action.notable.param.rule_description = This search looks for PowerShell requesting privileges consistent with credential dumping. +action.notable.param.rule_title = Detect Mimikatz Via PowerShell And EventCode 4703 action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2731,16 +2157,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=wineventlog_security signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = `wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` [ESCU - Detect New Local Admin account - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for newly created accounts that have been elevated to local administrators. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Valid Accounts", "Defense Evasion", "Persistence"], "nist": ["PR.AC", "DE.CM"]} -action.escu.eli5 = This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is "Administrators", and determines whether they are generated for the same user's Security ID within three hours of each other. It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1078"], "nist": ["PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for newly created accounts that have been elevated to local administrators. action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.\ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Security ID, **Field:** Security_ID\ 1. \ @@ -2749,36 +2175,26 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not 1. **Label:** Message, **Field:** Message\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives -action.escu.creation_date = 2018-03-26 +action.escu.creation_date = 2019-02-28 action.escu.modification_date = 2019-02-28 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect New Local Admin account - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = 0 9 * * * -dispatch.earliest_time = -1440m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect New Local Admin account +action.correlationsearch.label = ESCU - Detect New Local Admin account - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user,src_user, dest -action.notable.param.rule_description = The new user account $user$ was created on $dest$ by $src_user$. -action.notable.param.rule_title = New local admin account $user$ created by $src_user$. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for newly created accounts that have been elevated to local administrators. +action.notable.param.rule_title = Detect New Local Admin account action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2787,9 +2203,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype = wineventlog_security signature_id=4720 OR (signature_id=4732 Group_Name= Administrators) | transaction Security_ID connected=false maxspan=180m | search signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID Group_Name src_user Message +search = `wineventlog_security` signature_id=4720 OR (signature_id=4732 Group_Name= Administrators) | transaction Security_ID connected=false maxspan=180m | search signature_id=4720 signature_id=4732 | table _time user dest signature_id Security_ID Group_Name src_user Message | `detect_new_local_admin_account_filter` [ESCU - Detect New Login Attempts to Routers - Rule] action.escu = 0 @@ -2797,39 +2212,29 @@ action.escu.enabled = 1 description = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Authentication"] -action.escu.eli5 = Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a "router" in the ES assets and identity framework. +action.escu.eli5 = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. action.escu.how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. action.escu.known_false_positives = Legitimate router connections may appear as new connections -action.escu.creation_date = 2017-07-18 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect New Login Attempts to Routers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Active Directory", "Palo Alto Firewall"] -action.escu.analytic_story = ["Router & Infrastructure Security"] -cron_schedule = 0 0 * * * -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Router and Infrastructure Security"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect New Login Attempts to Routers +action.correlationsearch.label = ESCU - Detect New Login Attempts to Routers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = This search detects new connections made to the router devices at $dest$ -action.notable.param.rule_title = Detected a New Router Login +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. +action.notable.param.rule_title = Detect New Login Attempts to Routers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2838,48 +2243,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` +search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` [ESCU - Detect New Open S3 buckets - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.eli5 = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where a user has created an open/public S3 bucket. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. action.escu.creation_date = 2018-07-25 action.escu.modification_date = 2018-07-25 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule action.escu.search_type = detection -action.escu.asset_at_risk = S3 Bucket -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect New Open S3 buckets +action.correlationsearch.label = ESCU - Detect New Open S3 buckets - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = An open/public S3 bucket, $bucketName$, was created by $user$. -action.notable.param.rule_title = Public S3 bucket $bucketName$ created by $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. +action.notable.param.rule_title = Detect New Open S3 buckets action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user,bucketName -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2888,49 +2283,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user +search = `cloudtrail` AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user | `detect_new_open_s3_buckets_filter` -[ESCU - Detect Oulook.exe writing a .zip file - Rule] +[ESCU - Detect Oulook exe writing a zip file - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. -action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we are essentially trying to detect if outlook.exe is writing a `.zip` file to the disk. The way this search would run is, it will execute the the subsearch first which looks for all .zip files being written to the disk and outputs a crucial field "process_id", that we use the main search to check if that process\_id belongs to a process_name of outlook.exe. The search uses a join command to essentially give you an end result of the first and last time that zip file was written by outlook.exe, the dest and user logged on the system, the hash value and the complete path to the zip file on disk +action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. action.escu.how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. action.escu.known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk. -action.escu.creation_date = 2019-04-29 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high -action.escu.full_search_name = ESCU - Detect Oulook.exe writing a .zip file - Rule +action.escu.full_search_name = ESCU - Detect Oulook exe writing a zip file - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Phishing Payloads"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Oulook.exe writing a .zip file +action.correlationsearch.label = ESCU - Detect Oulook exe writing a zip file - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, file_name -action.notable.param.rule_description = Outlook.exe is writing a zip file $file_name$ on $dest$ -action.notable.param.rule_title = Outlook.exe is writing a zip file $file_name$ on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. +action.notable.param.rule_title = Detect Oulook exe writing a zip file action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2939,49 +2323,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `outlook_writing_zip_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_oulook_exe_writing_a__zip_file_filter` [ESCU - Detect Outbound SMB Traffic - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. -action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Commonly Used Port", "Credential Access", "Lateral Movement"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private. +action.escu.eli5 = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model action.escu.known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. -action.escu.creation_date = 2018-03-20 +action.escu.creation_date = 2020-01-22 action.escu.modification_date = 2020-01-22 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Outbound SMB Traffic - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Hidden Cobra Malware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Outbound SMB Traffic +action.correlationsearch.label = ESCU - Detect Outbound SMB Traffic - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = Outbound SMB network traffic detected. -action.notable.param.rule_title = Outbound SMB traffic from $src_ip$ to $dest_ip$ detected +action.notable.param.rule_description = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. +action.notable.param.rule_title = Detect Outbound SMB Traffic action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -2990,49 +2362,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `outbound_smb_connections_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_outbound_smb_traffic_filter` -[ESCU - Detect Path Interception By Creation Of program.exe - Rule] +[ESCU - Detect Path Interception By Creation Of program exe - Rule] action.escu = 0 action.escu.enabled = 1 description = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file "program.exe" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes. +action.escu.eli5 = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. action.escu.known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user. -action.escu.creation_date = 2017-11-16 +action.escu.creation_date = 2018-11-15 action.escu.modification_date = 2018-11-15 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Detect Path Interception By Creation Of program.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Detect Path Interception By Creation Of program exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Path Interception By Creation Of program.exe +action.correlationsearch.label = ESCU - Detect Path Interception By Creation Of program exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_path, file_name -action.notable.param.rule_description = A potentially malicious file program.exe was detected on the C: drive. The creation of this file is often associated with a motive to perform a path interception attack. -action.notable.param.rule_title = Path Interception attempt discovered $dest$ via creation of program.exe +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. +action.notable.param.rule_title = Detect Path Interception By Creation Of program exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, file_path, file_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3041,49 +2402,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where Filesystem.file_path="C:\\program.exe" by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where Filesystem.file_path="C:\\program.exe" by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `detect_path_interception_by_creation_of_program_exe_filter` -[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. +action.escu.eli5 = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. action.escu.known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -action.escu.creation_date = 2017-10-07 +action.escu.creation_date = 2020-02-03 action.escu.modification_date = 2020-02-03 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "parent_process_name", "user"] -action.escu.entities = ["dest", "parent_process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Prohibited Applications Spawning cmd.exe +action.correlationsearch.label = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process -action.notable.param.rule_description = A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe -action.notable.param.rule_title = Prohibited application($parent_process_name$) used to launch cmd.exe on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. +action.notable.param.rule_title = Detect Prohibited Applications Spawning cmd exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, parent_process, user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3092,49 +2442,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] | `detect_prohibited_applications_spawning_cmd_exe_filter` [ESCU - Detect PsExec With accepteula Flag - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we are looking for the PsExec process with `accepteula` on the command line. +action.escu.eli5 = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -action.escu.creation_date = 2018-03-28 +action.escu.creation_date = 2019-02-26 action.escu.modification_date = 2019-02-26 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] -action.escu.analytic_story = ["DHS Report TA18-074A", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect PsExec With accepteula Flag +action.correlationsearch.label = ESCU - Detect PsExec With accepteula Flag - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest,process_name -action.notable.param.rule_description = The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$. -action.notable.param.rule_title = PsExec executed with accepteula flag on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. +action.notable.param.rule_title = Detect PsExec With accepteula Flag action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3143,49 +2482,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = PsExec.exe Processes.process = "*accepteula*" by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = PsExec.exe Processes.process = "*accepteula*" by Processes.process_name Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter` [ESCU - Detect Rare Executables - Rule] action.escu = 0 action.escu.enabled = 1 description = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. -action.escu.mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes. +action.escu.eli5 = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. action.escu.known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results. -action.escu.creation_date = 2016-08-09 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Rare Executables - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Unusual Processes"] -cron_schedule = 10 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Unusual Processes"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Rare Executables +action.correlationsearch.label = ESCU - Detect Rare Executables - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process -action.notable.param.rule_description = The process $process$ was detected running on $dest. This process is rare in your environment. -action.notable.param.rule_title = Rare Process $process$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. +action.notable.param.rule_title = Detect Rare Executables action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3194,48 +2522,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_whitelist`| table process ] | `rare_executables_on_endpoint_filter` +search = | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_whitelist`| table process ] | `detect_rare_executables_filter` [ESCU - Detect S3 access from a new IP - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. -action.escu.mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.eli5 = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource +action.escu.mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. action.escu.known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour -action.escu.creation_date = 2018-06-25 +action.escu.creation_date = 2018-06-28 action.escu.modification_date = 2018-06-28 -action.escu.confidence = low +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule action.escu.search_type = detection -action.escu.asset_at_risk = S3 Bucket -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect S3 access from a new IP +action.correlationsearch.label = ESCU - Detect S3 access from a new IP - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = bucket_name, src_ip -action.notable.param.rule_description = A remote IP, $src_ip$, has made a successful connection with an S3 $bucket_name$. -action.notable.param.rule_title = S3 bucket $bucketName$ was accessed by a new $src_ip$ +action.notable.param.rule_description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. +action.notable.param.rule_title = Detect S3 access from a new IP action.notable.param.security_domain = network -action.notable.param.severity = low -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = bucket_name, src_ip -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3244,27 +2561,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri +search = `aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` [ESCU - Detect Spike in AWS API Activity - Rule] action.escu = 0 action.escu.enabled = 1 description = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.eli5 = This search and its corresponding subsearch run through a series of steps, as per the following: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -3273,36 +2579,26 @@ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = -action.escu.creation_date = 2018-03-12 +action.escu.creation_date = 2018-04-09 action.escu.modification_date = 2018-04-09 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in AWS API Activity +action.correlationsearch.label = ESCU - Detect Spike in AWS API Activity - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of AWS API calls by $user$ was detected. -action.notable.param.rule_title = Spike in AWS API activity detected by $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. +action.notable.param.rule_title = Detect Spike in AWS API Activity action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3311,59 +2607,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = `cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` [ESCU - Detect Spike in Network ACL Activity - Rule] action.escu = 0 action.escu.enabled = 1 description = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. -action.escu.mappings = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Exfiltration"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.eli5 = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).\ -1. Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Count the number of API calls per Amazon Resource Name (ARN).\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is not necessary, and merge the rest of the data with the results of the stats command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. They do the same for the standard deviation--weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and set the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and return the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.mappings = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. -action.escu.creation_date = 2018-05-17 +action.escu.creation_date = 2018-05-21 action.escu.modification_date = 2018-05-21 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Spike in Network ACL Activity - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Network ACL Activity"] -cron_schedule = 10 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in Network ACL Activity +action.correlationsearch.label = ESCU - Detect Spike in Network ACL Activity - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of AWS API calls related to network ACLs by $user$ was detected. -action.notable.param.rule_title = Spike in AWS Network ACL activity detected by $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. +action.notable.param.rule_title = Detect Spike in Network ACL Activity action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3372,59 +2647,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `network_acl_events` [search sourcetype=aws:cloudtrail `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = `cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` [ESCU - Detect Spike in S3 Bucket deletion - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.eli5 = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ -1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ -1. Count the number of API calls per ARN.\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -action.escu.creation_date = 2018-07-17 +action.escu.creation_date = 2018-11-27 action.escu.modification_date = 2018-11-27 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule action.escu.search_type = detection -action.escu.asset_at_risk = S3 Bucket -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in S3 Bucket deletion +action.correlationsearch.label = ESCU - Detect Spike in S3 Bucket deletion - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of S3 buckets deleted by $user$ was detected. -action.notable.param.rule_title = Spike detected in S3 bucket deletion activity by $user$. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. +action.notable.param.rule_title = Detect Spike in S3 Bucket deletion action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3433,59 +2687,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` [ESCU - Detect Spike in Security Group Activity - Rule] action.escu = 0 action.escu.enabled = 1 description = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.eli5 = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls specifically for security groups.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -action.escu.creation_date = 2018-04-17 +action.escu.creation_date = 2018-04-18 action.escu.modification_date = 2018-04-18 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Spike in Security Group Activity - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in Security Group Activity +action.correlationsearch.label = ESCU - Detect Spike in Security Group Activity - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of AWS API calls related to security groups by $user$ was detected. -action.notable.param.rule_title = Spike in AWS Security Group activity detected by $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. +action.notable.param.rule_title = Detect Spike in Security Group Activity action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3494,57 +2727,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `security_group_api_calls` [search sourcetype=aws:cloudtrail `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = `cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` [ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] action.escu = 0 action.escu.enabled = 1 description = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -action.escu.eli5 = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ -1. Counts the number of blocked outbound connections by each source IP\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `numberOfBlockedConnections` as `latestCount`.\ -1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -action.escu.creation_date = 2018-04-26 +action.escu.creation_date = 2018-05-07 action.escu.modification_date = 2018-05-07 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS +action.correlationsearch.label = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = A spike in the blocked outbound connection is detected from source $src_ip$. -action.notable.param.rule_title = Spike in blocked outbound network connections from $src_ip$ detected. +action.notable.param.rule_description = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. +action.notable.param.rule_title = Detect Spike in blocked Outbound Traffic from your AWS action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3553,49 +2766,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip +search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` [ESCU - Detect USB device insertion - Rule] action.escu = 0 action.escu.enabled = 1 description = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Exfiltration"], "nist": ["PR.PT", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} action.escu.data_models = ["Change_Analysis"] -action.escu.eli5 = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device +action.escu.eli5 = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -action.escu.creation_date = 2017-08-03 +action.escu.creation_date = 2017-11-27 action.escu.modification_date = 2017-11-27 -action.escu.confidence = low +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect USB device insertion - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Data Protection"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect USB device insertion +action.correlationsearch.label = ESCU - Detect USB device insertion - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Read/Write attempt to a USB was detected on this host -action.notable.param.rule_title = Read/Write attempt to a USB detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. +action.notable.param.rule_title = Detect USB device insertion action.notable.param.security_domain = endpoint -action.notable.param.severity = low -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3604,49 +2806,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` +search = | tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` [ESCU - Detect Unauthorized Assets by MAC address - Rule] action.escu = 0 action.escu.enabled = 1 description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. -action.escu.mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["ID.AM", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Network_Sessions"] -action.escu.eli5 = This search requires you to leverage the Enterprise Security Assets and Identity framework to populate assets_by_str.csv. Once the assets_by_str.csv is populated, we then query your DHCP logs to detect unknown systems connecting to your network. More documentation is available at: http://docs.splunk.com/Documentation/ES/4.7.1/Admin/Verifyassetandidentitydata. +action.escu.eli5 = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. action.escu.how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. action.escu.known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. -action.escu.creation_date = 2017-06-11 +action.escu.creation_date = 2017-09-13 action.escu.modification_date = 2017-09-13 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Unauthorized Assets by MAC address - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Infrastructure -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Asset Tracking"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Unauthorized Assets by MAC address +action.correlationsearch.label = ESCU - Detect Unauthorized Assets by MAC address - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = The host $src$ issued a DHCP request to connect with your network that does not belong to the list of authorized devices -action.notable.param.rule_title = Unauthorized Asset found with mac address: $src_mac$ +action.notable.param.rule_description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +action.notable.param.rule_title = Detect Unauthorized Assets by MAC address action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_mac,src_ip -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3655,49 +2845,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.src_mac | dedup All_Sessions.src_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as src_mac | fields + src_mac] +search = | tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.src_mac | dedup All_Sessions.src_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as src_mac | fields + src_mac] | `detect_unauthorized_assets_by_mac_address_filter` -[ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule] +[ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers often leverage various scripting languages to execute their attacks. In a Windows environment, the Windows Script Host is the tool that interprets the scripts and is included in all modern versions of Windows. The Windows Script Host is available as a command-line tool called "cscript.exe" or "wscript.exe." To detect this behavior, the search looks for process-creation events for cscript.exe or wscript.exe with a parent process of cmd.exe. The search will return the count, the first and last times this behavior was seen on a destination machine, and user and process information. +action.escu.eli5 = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = Some legitimate applications may exhibit this behavior. -action.escu.creation_date = 2017-10-09 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Suspicious Command-Line Executions"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Suspicious Command-Line Executions"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Use of cmd.exe to Launch Script Interpreters +action.correlationsearch.label = ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, parent_process -action.notable.param.rule_description = Potentially malicious script execution detected. -action.notable.param.rule_title = Command prompt is executing scripts on $dest$ using $process_name$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine +action.notable.param.rule_title = Detect Use of cmd exe to Launch Script Interpreters action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3706,49 +2885,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` [ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. -action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery", "System Information Discovery"]} +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} action.escu.data_models = ["Web"] -action.escu.eli5 = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +action.escu.eli5 = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. action.escu.how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -action.escu.creation_date = 2016-10-04 +action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Web Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers +action.correlationsearch.label = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = -action.notable.param.rule_description = This search looks for specific GET/HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,url -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3757,17 +2925,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` [ESCU - Detect hosts connecting to dynamic domain providers - Rule] action.escu = 0 action.escu.enabled = 1 description = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = The search leverages an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query. It checks the results against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` by each host (DNS.src). +action.escu.eli5 = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. action.escu.how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -3776,36 +2943,25 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -action.escu.creation_date = 2017-11-17 +action.escu.creation_date = 2020-01-16 action.escu.modification_date = 2020-01-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect hosts connecting to dynamic domain providers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "DNS Hijacking", "Data Protection", "Dynamic DNS", "Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Data Protection", "Prohibited Traffic Allowed or Protocol Mismatch", "DNS Hijacking", "Suspicious DNS Traffic", "Dynamic DNS", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect hosts connecting to dynamic domain providers +action.correlationsearch.label = ESCU - Detect hosts connecting to dynamic domain providers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = answer, src, query -action.notable.param.rule_description = The search has detected a host making outbound queries to Dynamic DNS providers -action.notable.param.rule_title = Host $src$ detected to make a query to a Dynamic DNS provider +action.notable.param.rule_description = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. +action.notable.param.rule_title = Detect hosts connecting to dynamic domain providers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src, query -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3814,49 +2970,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` +search = | tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` [ESCU - Detect malicious requests to exploit JBoss servers - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. -action.escu.mappings = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} action.escu.data_models = ["Web"] -action.escu.eli5 = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +action.escu.eli5 = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. action.escu.how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model action.escu.known_false_positives = No known false positives for this detection. -action.escu.creation_date = 2016-10-04 +action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Web Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect malicious requests to exploit JBoss servers +action.correlationsearch.label = ESCU - Detect malicious requests to exploit JBoss servers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, dest_ip -action.notable.param.rule_description = A search for detecting malicious requests made to exploit jmx-console in JBoss servers. The bad requests have a long url length since it serves the payload via the url -action.notable.param.rule_title = Detected malicious requests to exploit JBoss servers +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. +action.notable.param.rule_title = Detect malicious requests to exploit JBoss servers action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,url,src -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3865,49 +3010,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` -[ESCU - Detect mshta.exe running scripts in command-line arguments - Rule] +[ESCU - Detect mshta exe running scripts in command-line arguments - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Mshta.exe is a built-in Windows utility that can launch HTML files with .hta extensions (HTML applications), javascript, or VBScript. The search detects this behavior by looking for events where the process mshta.exe is executed with command-line arguments that indicate that a script is invoked +action.escu.eli5 = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -action.escu.creation_date = 2018-08-07 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Detect mshta.exe running scripts in command-line arguments - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Detect mshta exe running scripts in command-line arguments - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious MSHTA Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect mshta.exe running scripts in command-line arguments +action.correlationsearch.label = ESCU - Detect mshta exe running scripts in command-line arguments - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process_name -action.notable.param.rule_description = Mshta.exe is seen to be executing scripts via the command-line arguments -action.notable.param.rule_title = Mshta.exe is executing scripts on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. +action.notable.param.rule_title = Detect mshta exe running scripts in command-line arguments action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process, parent_process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3916,48 +3050,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*vbscript* OR process=*javascript*) +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*vbscript* OR process=*javascript*) | `detect_mshta_exe_running_scripts_in_command_line_arguments_filter` [ESCU - Detect new API calls from user roles - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. +action.escu.data_models = [] +action.escu.eli5 = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. action.escu.known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. -action.escu.creation_date = 2018-04-01 +action.escu.creation_date = 2018-04-16 action.escu.modification_date = 2018-04-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect new API calls from user roles - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS User Monitoring"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect new API calls from user roles +action.correlationsearch.label = ESCU - Detect new API calls from user roles - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A new API call made by $user$ has been detected. This API activity has either never been seen before or has not been seen within the last hour. -action.notable.param.rule_title = New API call by $user$ detected +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. +action.notable.param.rule_title = Detect new API calls from user roles action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -3966,48 +3090,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` +search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` [ESCU - Detect new user AWS Console Login - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -action.escu.creation_date = 2018-02-26 +action.escu.creation_date = 2018-04-30 action.escu.modification_date = 2018-04-30 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detect new user AWS Console Login - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious AWS Login Activities"] -cron_schedule = 5 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect new user AWS Console Login +action.correlationsearch.label = ESCU - Detect new user AWS Console Login - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A new user has logged into the AWS console -action.notable.param.rule_title = AWS Console Login by New User +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour +action.notable.param.rule_title = Detect new user AWS Console Login action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4016,49 +3130,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` [ESCU - Detect processes used for System Network Configuration Discovery - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. -action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers have a range of built-in Windows tools they leverage to ascertain the topography of a network from the point of view of a compromised machine. It is uncommon to see these commands execute quickly within short periods of time. This search returns the number of times, as well as the first time and last times, that every process has run for each endpoint. It then executes the macro `system_network_configuration_discovery_tools`, which looks for processes that are typically used for network configuration discovery. Once you have a list of suspicious process launches for each destination, you can leverage the transaction command to see what processes are fired within a five-minute span on an endpoint and detect only those events where the count of these processes is greater than five. +action.escu.eli5 = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. action.escu.how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. action.escu.known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. -action.escu.creation_date = 2018-11-04 +action.escu.creation_date = 2018-11-20 action.escu.modification_date = 2018-11-20 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect processes used for System Network Configuration Discovery - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual Processes"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect processes used for System Network Configuration Discovery +action.correlationsearch.label = ESCU - Detect processes used for System Network Configuration Discovery - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = Fast execution of processes $related to network system configuration discovery seen on $dest$. -action.notable.param.rule_title = Fast execution of processes $process_name$ related to network discovery seen on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. +action.notable.param.rule_title = Detect processes used for System Network Configuration Discovery action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4067,17 +3170,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` [ESCU - Detect web traffic to dynamic domain providers - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for web connections to dynamic DNS providers. -action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Web Service", "Exfiltration Over Command and Control Channel", "Defense Evasion"], "nist": ["PR.IP", "DE.DP"]} +action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"]} action.escu.data_models = ["Web"] -action.escu.eli5 = This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred. +action.escu.eli5 = This search looks for web connections to dynamic DNS providers. action.escu.how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` @@ -4087,31 +3189,21 @@ action.escu.modification_date = 2018-09-06 action.escu.confidence = high action.escu.full_search_name = ESCU - Detect web traffic to dynamic domain providers - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Dynamic DNS"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect web traffic to dynamic domain providers +action.correlationsearch.label = ESCU - Detect web traffic to dynamic domain providers - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, url, dest -action.notable.param.rule_description = The host $src$ has been detected making a web request to $url$, which is a listed as a dynamic DNS provider. -action.notable.param.rule_title = Dynamic DNS web traffic detected on $src$. +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for web connections to dynamic DNS providers. +action.notable.param.rule_title = Detect web traffic to dynamic domain providers action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src, url , dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4120,49 +3212,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` +search = | tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` [ESCU - Detection of DNS Tunnels - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. -action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.PT", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "PR.DS"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = The search will calculate the distinct count and sum of the length of DNS queries made and DNS answers received by a particular host to alert the analyst if the combined length is greater than 10000, which is not typical behavior. +action.escu.eli5 = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. action.escu.how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. action.escu.known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. -action.escu.creation_date = 2017-07-19 +action.escu.creation_date = 2017-09-18 action.escu.modification_date = 2017-09-18 -action.escu.confidence = low +action.escu.confidence = high action.escu.full_search_name = ESCU - Detection of DNS Tunnels - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Data Protection", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detection of DNS Tunnels +action.correlationsearch.label = ESCU - Detection of DNS Tunnels - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = Potential DNS tunnel detected from $src$ which may be exfiltrating large data -action.notable.param.rule_title = DNS tunnel detected on $src$ +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. +action.notable.param.rule_title = Detection of DNS Tunnels action.notable.param.security_domain = network -action.notable.param.severity = low -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src, query -alert.suppress.period = 43200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4171,49 +3252,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 +search = | tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` [ESCU - Detection of tools built by NirSoft - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Discovery", "Execution", "Lateral Movement", "Third-party Software", "Account Discovery"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072", "T1087"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for process-creation events accompanied by specific command-line arguments ("scomma" and "stext"). These parameters may be leveraged by a set of free, legitimate tools built by NirSoft. Attackers have been seen abusing the tools' capabilities to steal passwords, set up key loggers, recover account information from mail clients, and conduct other nefarious activities. The search will identify the count, the first and last times a process is executed, the command-line arguments, and the parent process. +action.escu.eli5 = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. -action.escu.creation_date = 2018-09-11 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Detection of tools built by NirSoft - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A "] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detection of tools built by NirSoft +action.correlationsearch.label = ESCU - Detection of tools built by NirSoft - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for specific arguments passed via the command line and detects execution of tools built by NirSoft, which are often abused by attackers. -action.notable.param.rule_title = Potential abuse of NirSoft tools on $dest$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. +action.notable.param.rule_title = Detection of tools built by NirSoft action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4222,49 +3292,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` [ESCU - Disabling Remote User Account Control - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search checks to see if the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy was modified. This registry key can be used to disable remote User Account Control. The search returns the count, the first time activity was seen, last time activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. action.escu.known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. -action.escu.creation_date = 2017-10-12 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Disabling Remote User Account Control - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Disabling Remote User Account Control +action.correlationsearch.label = ESCU - Disabling Remote User Account Control - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, registry_path -action.notable.param.rule_description = The registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy was modified. This registry key is associated with disabling remote UAC on Windows. -action.notable.param.rule_title = Registry Key Associated With Disabling Remote UAC Modified on $dest$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). +action.notable.param.rule_title = Disabling Remote User Account Control action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user, registry_path -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4273,17 +3332,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*Windows\\CurrentVersion\\Policies\\System\\LocalAccountTokenFilterPolicy" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*Windows\\CurrentVersion\\Policies\\System\\LocalAccountTokenFilterPolicy" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` [ESCU - Dump LSASS via comsvcs DLL - Rule] action.escu = 0 action.escu.enabled = 1 description = Detect the usage of comsvcs.dll for dumping the lsass process. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = LSASS is the Local Security Authority Subsystem Service, which is responsible for storing the user credentials. There are multiple ways to attack LSASS. This search detects the usage of comsvcs.dll for dumping the LSASS process. +action.escu.eli5 = Detect the usage of comsvcs.dll for dumping the lsass process. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified. action.escu.creation_date = 2020-02-21 @@ -4291,31 +3349,21 @@ action.escu.modification_date = 2020-02-21 action.escu.confidence = high action.escu.full_search_name = ESCU - Dump LSASS via comsvcs DLL - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Dump LSASS via comsvcs DLL +action.correlationsearch.label = ESCU - Dump LSASS via comsvcs DLL - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, process_name, process -action.notable.param.rule_description = An attempt to dump credentials of lsass by $user$ was detected. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Detect the usage of comsvcs.dll for dumping the lsass process. action.notable.param.rule_title = Dump LSASS via comsvcs DLL action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4324,7 +3372,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter` @@ -4333,39 +3380,30 @@ action.escu = 0 action.escu.enabled = 1 description = This search looks for EC2 instances being modified by users who have not previously modified them. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2_modification_api_calls`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems. +action.escu.data_models = [] +action.escu.eli5 = This search looks for EC2 instances being modified by users who have not previously modified them. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. action.escu.creation_date = 2018-04-09 action.escu.modification_date = 2018-04-09 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Modified With Previously Unseen User +action.correlationsearch.label = ESCU - EC2 Instance Modified With Previously Unseen User - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Modified By Previously Unseen User $user$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for EC2 instances being modified by users who have not previously modified them. +action.notable.param.rule_title = EC2 Instance Modified With Previously Unseen User action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4374,48 +3412,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `ec2_modification_api_calls` [search sourcetype=aws:cloudtrail `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest +search = `cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` [ESCU - EC2 Instance Started In Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started -action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` +action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -action.escu.creation_date = 2018-02-01 +action.escu.creation_date = 2018-02-23 action.escu.modification_date = 2018-02-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - EC2 Instance Started In Previously Unseen Region - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["awsRegion"] -action.escu.entities = ["awsRegion"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started In Previously Unseen Region +action.correlationsearch.label = ESCU - EC2 Instance Started In Previously Unseen Region - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = awsRegion -action.notable.param.rule_description = An AWS instance is started in a new, previously unseen, region -action.notable.param.rule_title = AWS instance is started in a new region +action.notable.param.rule_description = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started +action.notable.param.rule_title = EC2 Instance Started In Previously Unseen Region action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = awsRegion -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = awsRegion -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4424,48 +3451,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" +search = `cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` [ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for EC2 instances being created with previously unseen AMIs. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns the AMI image ID of all successful EC2 instance launches within the last hour and then appends the historical data from the lookup file to those results. It then recalculates the earliest and latest seen time field for each AMI image ID and returns only those AMI image IDs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +action.escu.data_models = [] +action.escu.eli5 = This search looks for EC2 instances being created with previously unseen AMIs. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. action.escu.known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen AMI +action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The EC2 instance $dest$ was created with previously unused AMI $amiID$ -action.notable.param.rule_title = EC2 Instance Type $dest$ Created With New AMI +action.notable.param.rule_description = This search looks for EC2 instances being created with previously unseen AMIs. +action.notable.param.rule_title = EC2 Instance Started With Previously Unseen AMI action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4474,48 +3490,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` [ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for EC2 instances being created with previously unseen instance types. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +action.escu.data_models = [] +action.escu.eli5 = This search looks for EC2 instances being created with previously unseen instance types. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -action.escu.creation_date = 2018-03-12 +action.escu.creation_date = 2020-02-07 action.escu.modification_date = 2020-02-07 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type +action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = instanceType -action.notable.param.rule_description = The EC2 instance type $instanceType$ was used for the first time to create $dest$. -action.notable.param.rule_title = New EC2 Instance Type $instanceType$ detected +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for EC2 instances being created with previously unseen instance types. +action.notable.param.rule_title = EC2 Instance Started With Previously Unseen Instance Type action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4524,48 +3530,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` [ESCU - EC2 Instance Started With Previously Unseen User - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for EC2 instances being created by users who have not created them before. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.eli5 = The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +action.escu.data_models = [] +action.escu.eli5 = This search looks for EC2 instances being created by users who have not created them before. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. -action.escu.creation_date = 2018-03-15 +action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS Instance -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["AWS"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen User +action.correlationsearch.label = ESCU - EC2 Instance Started With Previously Unseen User - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Created By Previously Unseen User $user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for EC2 instances being created by users who have not created them before. +action.notable.param.rule_title = EC2 Instance Started With Previously Unseen User action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4574,51 +3570,39 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` [ESCU - Email Attachments With Lots Of Spaces - Rule] action.escu = 0 action.escu.enabled = 1 description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. -action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Email"] -action.escu.eli5 = This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name. +action.escu.eli5 = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. action.escu.known_false_positives = None at this time -action.escu.creation_date = 2017-04-21 +action.escu.creation_date = 2017-09-19 action.escu.modification_date = 2017-09-19 action.escu.confidence = high action.escu.full_search_name = ESCU - Email Attachments With Lots Of Spaces - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src", "message_id"] -action.escu.entities = ["src", "message_id"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Suspicious Emails"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Suspicious Emails"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Email Attachments With Lots Of Spaces +action.correlationsearch.label = ESCU - Email Attachments With Lots Of Spaces - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user, file_name -action.notable.param.rule_description = The sender $src_user$ has sent an email with a suspicious amount of spaces in the file name: $file_name$ -action.notable.param.rule_title = Suspicious Email Attachment from $src_user$ +action.notable.param.rule_description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. +action.notable.param.rule_title = Email Attachments With Lots Of Spaces action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4627,49 +3611,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" +search = | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` [ESCU - Email files written outside of the Outlook directory - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. +action.escu.eli5 = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. action.escu.known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -action.escu.creation_date = 2017-12-13 +action.escu.creation_date = 2020-03-23 action.escu.modification_date = 2020-03-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Email files written outside of the Outlook directory - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Email files written outside of the Outlook directory +action.correlationsearch.label = ESCU - Email files written outside of the Outlook directory - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_path, action, file_name -action.notable.param.rule_description = The system $dest$ has email files outside of the normal Outlook directory -action.notable.param.rule_title = Email files created or modified on $dest$ that are not in the normal Outlook directory +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. +action.notable.param.rule_title = Email files written outside of the Outlook directory action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, file_path -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4678,49 +3651,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_not_in_outlook_directory_filter` +search = | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` [ESCU - Email servers sending high volume traffic to hosts - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. action.escu.creation_date = 2017-12-20 action.escu.modification_date = 2017-12-20 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Email servers sending high volume traffic to hosts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest_ip"] -action.escu.entities = ["dest_ip"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = 0 0 * * * -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Email servers sending high volume traffic to hosts +action.correlationsearch.label = ESCU - Email servers sending high volume traffic to hosts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest_ip -action.notable.param.rule_description = High volume of traffic that originated from an email server is being sent to $dest_ip$ -action.notable.param.rule_title = High volume of traffic from an email server sent to $dest_ip$ +action.notable.param.rule_description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. +action.notable.param.rule_title = Email servers sending high volume traffic to hosts action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4729,49 +3690,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` [ESCU - Excessive DNS Failures - Rule] action.escu = 0 action.escu.enabled = 1 description = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Exfiltration Over Alternative Protocol", "Command and Control", "Commonly Used Port"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = This search looks at DNS traffic with a reply code that is NOT indicative of a successful response. Numerous unsuccessful replies may be indicative of DNS protocol tampering or other malicious activity. If more than 50 of these unsuccessful responses are observed over the time frame of the search, a notable event will be generated. +action.escu.eli5 = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. action.escu.known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2017-09-18 action.escu.modification_date = 2017-09-18 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Excessive DNS Failures - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Excessive DNS Failures +action.correlationsearch.label = ESCU - Excessive DNS Failures - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success and triggers on more than 50 occurrences. +action.notable.param.rule_description = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. action.notable.param.rule_title = Excessive DNS Failures action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src,query -alert.suppress.period = 43200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4780,49 +3729,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 +search = | tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` [ESCU - Execution of File With Spaces Before Extension - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension. +action.escu.eli5 = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = None identified. action.escu.creation_date = 2018-01-26 action.escu.modification_date = 2018-01-26 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Execution of File With Spaces Before Extension - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows File Extension and Association Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Execution of File With Spaces Before Extension +action.correlationsearch.label = ESCU - Execution of File With Spaces Before Extension - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ executed a file with spaces before its extension. -action.notable.param.rule_title = Process $process$ with spaces before extension Launched on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. +action.notable.param.rule_title = Execution of File With Spaces Before Extension action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4831,49 +3769,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` +search = | tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` [ESCU - Execution of File with Multiple Extensions - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. +action.escu.eli5 = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. action.escu.known_false_positives = None identified. -action.escu.creation_date = 2018-01-26 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 action.escu.confidence = high action.escu.full_search_name = ESCU - Execution of File with Multiple Extensions - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows File Extension and Association Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Execution of File with Multiple Extensions +action.correlationsearch.label = ESCU - Execution of File with Multiple Extensions - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process -action.notable.param.rule_description = The system $dest$ executed a file with a double extension. -action.notable.param.rule_title = Process With Multiple Extensions Launched on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. +action.notable.param.rule_title = Execution of File with Multiple Extensions action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4882,48 +3809,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter` [ESCU - Extended Period Without Successful Netbackup Backups - Rule] action.escu = 0 action.escu.enabled = 1 description = This search returns a list of hosts that have not successfully completed a backup in over a week. action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -action.escu.eli5 = This search finds all the successful backup messages in your logs, and then looks for the most recent backup time for each system. It then identifies those systems where the most recent successful backup time is over a week ago, and reports on them. +action.escu.data_models = [] +action.escu.eli5 = This search returns a list of hosts that have not successfully completed a backup in over a week. action.escu.how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-06-15 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 action.escu.confidence = high action.escu.full_search_name = ESCU - Extended Period Without Successful Netbackup Backups - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Netbackup"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor Backup Solution"] -cron_schedule = 0 0 1 * * -dispatch.earliest_time = -7d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Extended Period Without Successful Netbackup Backups +action.correlationsearch.label = ESCU - Extended Period Without Successful Netbackup Backups - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ has not had a successful backup for an extended period. -action.notable.param.rule_title = Extended period of no successful backups by $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search returns a list of hosts that have not successfully completed a backup in over a week. +action.notable.param.rule_title = Extended Period Without Successful Netbackup Backups action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4932,17 +3849,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="netbackup_logs" MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest +search = `netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` [ESCU - File with Samsam Extension - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for file writes with extensions consistent with a SamSam ransomware attack. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at file modifications across your hosts and creates notable events when it identifies files with extensions associated with the SamSam ransomware, including `.stubbin`, `.berkshire`, `.satoshi`, `.sophos`, or `.keyxml`. Files with these extensions have been observed in SamSam attacks consisting of payload data or keying material. +action.escu.eli5 = The search looks for file writes with extensions consistent with a SamSam ransomware attack. action.escu.how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results. action.escu.creation_date = 2018-12-14 @@ -4950,31 +3866,21 @@ action.escu.modification_date = 2018-12-14 action.escu.confidence = high action.escu.full_search_name = ESCU - File with Samsam Extension - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = File with Samsam Extension +action.correlationsearch.label = ESCU - File with Samsam Extension - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file with an extension associated with SamSam ransomware was written on $dest$. -action.notable.param.rule_title = File with known SamSam extension detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for file writes with extensions consistent with a SamSam ransomware attack. +action.notable.param.rule_title = File with Samsam Extension action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -4983,48 +3889,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` [ESCU - First Time Seen Running Windows Service - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the first time a Windows service is seen running in your environment. -action.escu.mappings = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "New Service"], "mitre_technique_id": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} -action.escu.eli5 = This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name. +action.escu.mappings = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for the first time a Windows service is seen running in your environment. action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. action.escu.known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. -action.escu.creation_date = 2018-07-22 +action.escu.creation_date = 2020-01-13 action.escu.modification_date = 2020-01-13 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - First Time Seen Running Windows Service - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Service Abuse", "Orangeworm Attack Group"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = First Time Seen Running Windows Service +action.correlationsearch.label = ESCU - First Time Seen Running Windows Service - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = service_name -action.notable.param.rule_description = The service $service_name$ is running on $dest$. This is the first time this service has been run on any system. -action.notable.param.rule_title = First Time Seen Windows Service $service_name$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for the first time a Windows service is seen running in your environment. +action.notable.param.rule_title = First Time Seen Running Windows Service action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = service_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5033,49 +3929,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" [ search eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | inputlookup append=t previously_seen_running_windows_services | stats min(firstTime) as firstTime max(lastTime) as lastTime by service_name | outputlookup previously_seen_running_windows_services| eval serviceStatus=if(firstTime >= relative_time(now(),"-60m@m"), "First time seen Windows service","Previously seen Windows service") | where serviceStatus="First time seen Windows service"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table service_name]| table _time dest service_name +search = `wineventlog_system` signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" [ search `wineventlog_system` signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | inputlookup append=t previously_seen_running_windows_services | stats min(firstTime) as firstTime max(lastTime) as lastTime by service_name | outputlookup previously_seen_running_windows_services| eval serviceStatus=if(firstTime >= relative_time(now(),"-60m@m"), "First time seen Windows service","Previously seen Windows service") | where serviceStatus="First time seen Windows service"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table service_name]| table _time dest service_name | `first_time_seen_running_windows_service_filter` [ESCU - First time seen command line argument - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. +action.escu.eli5 = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. action.escu.known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -action.escu.creation_date = 2018-04-09 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - First time seen command line argument - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Hidden Cobra Malware", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Hidden Cobra Malware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = First time seen command line argument +action.correlationsearch.label = ESCU - First time seen command line argument - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = The system $dest$ executed a command-line argument, $process$, that has not previously been seen. -action.notable.param.rule_title = First-time seen command-line argument was detected on $dest$. +action.notable.param.rule_description = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. +action.notable.param.rule_title = First time seen command line argument action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5084,45 +3968,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_cmd_line_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` [ESCU - GCP GCR container uploaded - Rule] action.escu = 0 action.escu.enabled = 1 description = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. -action.escu.mappings = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = In this search we can detect if a new container has been uploaded to Google Container Registry, operator can monitor users uploading containers, object paths of new uploaded containers. +action.escu.mappings = {} +action.escu.data_models = [] +action.escu.eli5 = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. action.escu.known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. action.escu.creation_date = 2020-02-20 action.escu.modification_date = 2020-02-20 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - GCP GCR container uploaded - Rule action.escu.search_type = detection -action.escu.asset_at_risk = GCP GCR Container -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["GCP"] -action.escu.analytic_story = ["Container Implantation Monitoring & Investigation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = GCP GCR container uploaded +action.correlationsearch.label = ESCU - GCP GCR container uploaded - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user, account, bucket_name, event_name -action.notable.param.rule_description = An GCP GCR container uploaded by $src_user$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. action.notable.param.rule_title = GCP GCR container uploaded action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high +alert.digest_mode = 1 action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5131,44 +4008,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `container_implant_gcp_detection_filter` +search = |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` [ESCU - GCP Kubernetes cluster scan detection - Rule] action.escu = 0 action.escu.enabled = 1 description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster -action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} -action.escu.eli5 = In this search we can detect unauthenticated web requests and possible attack against a GCP cluster, by looking at k8s authentication data, user agent, source IPs and destionation +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -action.escu.creation_date = 2020-03-24 +action.escu.creation_date = 2020-04-15 action.escu.modification_date = 2020-04-15 action.escu.confidence = high action.escu.full_search_name = ESCU - GCP Kubernetes cluster scan detection - Rule action.escu.search_type = detection -action.escu.asset_at_risk = GCP Kubernetes cluster -action.escu.fields_required = ["cluster_name", "src_ip"] -action.escu.entities = ["cluster_name", "src_ip"] -action.escu.providing_technologies = ["GCP"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Kubernetes Scanning Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = GCP Kubernetes cluster scan detection +action.correlationsearch.label = ESCU - GCP Kubernetes cluster scan detection - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, -action.notable.param.rule_description = Detect scanning attempts against a GCP Kubernetes cluster +action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster action.notable.param.rule_title = GCP Kubernetes cluster scan detection action.notable.param.security_domain = threat action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = cluster_name -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +alert.digest_mode = 1 action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5177,49 +4047,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kubernetes_gcp_scan_fingerprint_detection` +search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` -[ESCU - Hiding Files And Directories With Attrib.exe - Rule] +[ESCU - Hiding Files And Directories With Attrib exe - Rule] action.escu = 0 action.escu.enabled = 1 description = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set. The +h flag is used to hide a file. +action.escu.eli5 = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. -action.escu.creation_date = 2017-10-23 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Hiding Files And Directories With Attrib.exe - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Hiding Files And Directories With Attrib exe - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Hiding Files And Directories With Attrib.exe +action.correlationsearch.label = ESCU - Hiding Files And Directories With Attrib exe - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = Attrib.exe is often used by attackers to hide malware files and directories in windows environments. This rule detects command-line arguments used to hide a file/directory -action.notable.param.rule_title = Suspicious usage of attrib.exe on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. +action.notable.param.rule_title = Hiding Files And Directories With Attrib exe action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5228,49 +4087,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `attrib_to_hide_files_filter` +search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` [ESCU - Hosts receiving high volume of network traffic from email server - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. action.escu.creation_date = 2017-12-20 action.escu.modification_date = 2017-12-20 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Hosts receiving high volume of network traffic from email server - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = 0 0 * * * -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Hosts receiving high volume of network traffic from email server +action.correlationsearch.label = ESCU - Hosts receiving high volume of network traffic from email server - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = $src_ip$ receiving high volume of traffic that originated from an email server -action.notable.param.rule_title = High volume traffic from email server received by $src_ip$ +action.notable.param.rule_description = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. +action.notable.param.rule_title = Hosts receiving high volume of network traffic from email server action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5279,49 +4126,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average +search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` [ESCU - Identify New User Accounts - Rule] action.escu = 0 action.escu.enabled = 1 description = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} -action.escu.data_models = ["Identity_Management"] -action.escu.eli5 = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. action.escu.how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. action.escu.known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -action.escu.creation_date = 2017-08-05 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Identify New User Accounts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Domain Server -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Active Directory"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = 0 0 * * * -dispatch.earliest_time = -24h@h -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Identify New User Accounts +action.correlationsearch.label = ESCU - Identify New User Accounts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = Using the identities lookup and macro from Enterprise Security to identify (report) new users (6 month period) and temp users (3 months until account expiration) -action.notable.param.rule_title = Identify Temporary Users +action.notable.param.rule_description = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. +action.notable.param.rule_title = Identify New User Accounts action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = identity -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5330,9 +4165,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate +search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` [ESCU - Large Volume of DNS ANY Queries - Rule] action.escu = 0 @@ -5340,39 +4174,28 @@ action.escu.enabled = 1 description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. action.escu.mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. +action.escu.eli5 = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. action.escu.known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. -action.escu.creation_date = 2016-08-24 +action.escu.creation_date = 2017-09-20 action.escu.modification_date = 2017-09-20 action.escu.confidence = high action.escu.full_search_name = ESCU - Large Volume of DNS ANY Queries - Rule action.escu.search_type = detection -action.escu.asset_at_risk = DNS Servers -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DNS Amplification Attacks"] -cron_schedule = */5 * * * * -dispatch.earliest_time = -15m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Large Volume of DNS ANY Queries +action.correlationsearch.label = ESCU - Large Volume of DNS ANY Queries - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest action.notable.param.rule_description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. action.notable.param.rule_title = Large Volume of DNS ANY Queries action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 7200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5381,49 +4204,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` [ESCU - MacOS - Re-opened Applications - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Persistence"], "mitre_technique_id": ["T1164"], "nist": ["DE.DP", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks at the Endpoint data model to identify any MacOS process events referencing a property list file which determines which applications are "re-opened" during startup. This could indicate a malicious attempt to establish persistence on the system. +action.escu.eli5 = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. action.escu.known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. action.escu.creation_date = 2020-02-07 action.escu.modification_date = 2020-02-07 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - MacOS - Re-opened Applications - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["host"] -action.escu.entities = ["host"] -action.escu.providing_technologies = ["OSquery"] +action.escu.providing_technologies = [] action.escu.analytic_story = [] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = MacOS - Re-opened Applications +action.correlationsearch.label = ESCU - MacOS - Re-opened Applications - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = host -action.notable.param.rule_description = Observed process referencing MacOS com.apple.loginwindow.plist files for Host $host$ -action.notable.param.rule_title = Possible attempt to establish persistence on $host$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. +action.notable.param.rule_title = MacOS - Re-opened Applications action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = host -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = host -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5432,49 +4244,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` [ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes running with specific command-line arguments that indicate that the process will download a file from the Internet without display anything to the user. The search for "*-Exec*" is to check and see if the default execution policy for PowerShell is being overridden on the command-line. The search for "*-WindowStyle*" and "*hidden*" are to see if the window that would normally be displayed will be hidden from the user instead. Finally, the search for "*New-Object*" and "*System.Net.WebClient*" are there to check to see if a PowerShell object that can be used to download files will be created. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +action.escu.eli5 = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -action.escu.creation_date = 2016-09-18 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Connect To Internet With Hidden Window +action.correlationsearch.label = ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that connects to the Internet with a hidden window. -action.notable.param.rule_title = Malicious PowerShell Process detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. +action.notable.param.rule_title = Malicious PowerShell Process - Connect To Internet With Hidden Window action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5483,49 +4284,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*-Exec*" process="*-WindowStyle*" process="*hidden*" process="*New-Object*" process="*System.Net.WebClient*" +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*-Exec*" process="*-WindowStyle*" process="*hidden*" process="*New-Object*" process="*System.Net.WebClient*" | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` [ESCU - Malicious PowerShell Process - Encoded Command - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +action.escu.eli5 = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = System administrators may use this option, but it's not common. -action.escu.creation_date = 2016-09-18 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Encoded Command +action.correlationsearch.label = ESCU - Malicious PowerShell Process - Encoded Command - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has an encoded command on the command-line -action.notable.param.rule_title = PowerShell process with an encoded command detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. +action.notable.param.rule_title = Malicious PowerShell Process - Encoded Command action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user, process_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5534,51 +4324,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* | `malicious_powershell_process___encoded_command_filter` [ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to "Restricted," which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for `* -ex*`, which should catch all variations of this parameter, followed by the keyword `bypass`. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process. +action.escu.eli5 = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. -action.escu.creation_date = 2018-03-19 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_id", "process", "parent_process_id"] -action.escu.entities = ["dest", "process_id", "process", "parent_process_id"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = 50 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Execution Policy Bypass +action.correlationsearch.label = ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell process with parameters to bypass the local execution policy. -action.notable.param.rule_title = PowerShell process with -executionpolicy bypass detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. +action.notable.param.rule_title = Malicious PowerShell Process - Execution Policy Bypass action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.drilldown_name = View powershell process information on $dest$ -action.notable.param.drilldown_search = | from datamodel:Endpoint.Processes | search dest="$dest$" process_id=$process_id$ -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5587,49 +4364,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe AND (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe AND (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` [ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +action.escu.eli5 = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -action.escu.creation_date = 2016-09-18 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = 50 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +action.correlationsearch.label = ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell that had an encoded command on the command-line, attempted to bypass local execution policy, and prevented the display of an interactive prompt to the user. -action.notable.param.rule_title = PowerShell process with multiple suspicious command-line arguments detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. +action.notable.param.rule_title = Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5638,49 +4404,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI* +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI* | `malicious_powershell_process___multiple_suspicious_command_line_arguments_filter` [ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands. +action.escu.eli5 = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = These characters might be legitimately on the command-line, but it is not common. -action.escu.creation_date = 2017-04-25 +action.escu.creation_date = 2020-01-17 action.escu.modification_date = 2020-01-17 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Malicious PowerShell"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process With Obfuscation Techniques +action.correlationsearch.label = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name, process -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has evidence of obfuscation on the command-line -action.notable.param.rule_title = PowerShell process with an obfuscation techniques detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. +action.notable.param.rule_title = Malicious PowerShell Process With Obfuscation Techniques action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process_name,process -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5689,9 +4444,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, "`"))-1) + (mvcount(split(process, "^"))-1) | `malicious_powershell_process_obfuscation_techniques_output_filter` | search num_obfuscation > 0 +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, "`"))-1) + (mvcount(split(process, "^"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 0 [ESCU - Monitor DNS For Brand Abuse - Rule] action.escu = 0 @@ -5699,39 +4453,28 @@ action.escu.enabled = 1 description = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. action.escu.mappings = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} action.escu.data_models = ["Network_Resolution"] -action.escu.eli5 = This search gathers all the answers to each system's DNS query, then filters out all queries that do not appear on the list of faux "look-a-like" domains that have been generated from the brand abuse domains you are monitoring. +action.escu.eli5 = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. action.escu.how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time -action.escu.creation_date = 2017-06-01 +action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 action.escu.confidence = high action.escu.full_search_name = ESCU - Monitor DNS For Brand Abuse - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Brand Monitoring"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Monitor DNS For Brand Abuse +action.correlationsearch.label = ESCU - Monitor DNS For Brand Abuse - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = The host $src$ issued a DNS request for a domain to that which you are monitoring for brand abuse. -action.notable.param.rule_title = DNS Query Brand Abuse from $src$ +action.notable.param.rule_description = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. +action.notable.param.rule_title = Monitor DNS For Brand Abuse action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src,query -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5740,9 +4483,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` +search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` [ESCU - Monitor Email For Brand Abuse - Rule] action.escu = 0 @@ -5750,39 +4492,28 @@ action.escu.enabled = 1 description = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Email"] -action.escu.eli5 = This search looks at the sender address in email headers, and identifies those with a sender address using a domain name that matches the list of permutations generated for the domain you want to monitor. +action.escu.eli5 = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. action.escu.how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time -action.escu.creation_date = 2017-06-01 +action.escu.creation_date = 2018-01-05 action.escu.modification_date = 2018-01-05 action.escu.confidence = high action.escu.full_search_name = ESCU - Monitor Email For Brand Abuse - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["Microsoft Exchange", "Bro", "Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Monitor Email For Brand Abuse +action.correlationsearch.label = ESCU - Monitor Email For Brand Abuse - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user, message_id -action.notable.param.rule_description = The sender $src_user$ has sent an email from a similar domain to that which you are monitoring for brand abuse. -action.notable.param.rule_title = Possible Brand Abuse from $src_user$ +action.notable.param.rule_description = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. +action.notable.param.rule_title = Monitor Email For Brand Abuse action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = message_id, src_user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5791,49 +4522,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime +search = | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` [ESCU - Monitor Registry Keys for Print Monitors - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Local Port Monitor"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the `registry_path` field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry. +action.escu.mappings = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. action.escu.known_false_positives = You will encounter noise from legitimate print-monitor registry entries. -action.escu.creation_date = 2017-12-01 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Monitor Registry Keys for Print Monitors - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Monitor Registry Keys for Print Monitors +action.correlationsearch.label = ESCU - Monitor Registry Keys for Print Monitors - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, registry_path -action.notable.param.rule_description = A registry key associated with adding print monitors can potentially be misused by giving it a path of a malicious .dll in the registry. -action.notable.param.rule_title = Registry Key changes for Print Monitors detected on $dest$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. +action.notable.param.rule_title = Monitor Registry Keys for Print Monitors action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, registry_path -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5842,49 +4562,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` [ESCU - Monitor Web Traffic For Brand Abuse - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. -action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Web"] -action.escu.eli5 = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. +action.escu.eli5 = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time -action.escu.creation_date = 2017-06-01 +action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 action.escu.confidence = high action.escu.full_search_name = ESCU - Monitor Web Traffic For Brand Abuse - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Brand Monitoring"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Monitor Web Traffic For Brand Abuse +action.correlationsearch.label = ESCU - Monitor Web Traffic For Brand Abuse - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, url -action.notable.param.rule_description = The host $src$ connected to a web site with a domain similar to that which you are monitoring for brand abuse. -action.notable.param.rule_title = Web URL Brand Abuse from $src$ +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. +action.notable.param.rule_title = Monitor Web Traffic For Brand Abuse action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5893,16 +4602,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` +search = | tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` [ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} -action.escu.eli5 = This search detects instances when there are more than 5 distinct users failing Okta logins due to invalid credentails from the same IP address. This may be indicative of attack techniques such as credential stuffing or password spraying, where an attacker attempts to login using common or found passwords and attempts to authenticate with them. +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. action.escu.known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. action.escu.creation_date = 2020-04-01 @@ -5910,31 +4619,20 @@ action.escu.modification_date = 2020-04-01 action.escu.confidence = high action.escu.full_search_name = ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Infrastructure -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Okta"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Multiple Okta Users With Invalid Credentails From The Same IP +action.correlationsearch.label = ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = Multiple Users Failing Authenticaiton From $src_ip$ -action.notable.param.rule_title = Multiple Okta Users With Authentication Failures From a Single IP +action.notable.param.rule_description = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. +action.notable.param.rule_title = Multiple Okta Users With Invalid Credentails From The Same IP action.notable.param.security_domain = access action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = system -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5943,45 +4641,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=okta_log outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `okta_multiple_users_with_auth_failures_from_ip_filter` +search = `okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentails_from_the_same_ip_filter` [ESCU - New container uploaded to AWS ECR - Rule] action.escu = 0 action.escu.enabled = 1 description = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. -action.escu.mappings = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.eli5 = In this search we can detect if a new container has been uploaded to Amazon Elastic Container Registry, operator can monitor users uploading containers, image ids of new uploaded containers. +action.escu.mappings = {} +action.escu.data_models = [] +action.escu.eli5 = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. action.escu.known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. action.escu.creation_date = 2020-02-20 action.escu.modification_date = 2020-02-20 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - New container uploaded to AWS ECR - Rule action.escu.search_type = detection -action.escu.asset_at_risk = AWS ECR container -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Container Implantation Monitoring & Investigation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = New container uploaded to AWS ECR +action.correlationsearch.label = ESCU - New container uploaded to AWS ECR - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = image_id, src_user, region, user_type -action.notable.param.rule_description = A new container with image id $image_id$ uploaded to AWS ECR by user $src_user$ +action.notable.param.nes_fields = ['user', 'src'] +action.notable.param.rule_description = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. action.notable.param.rule_title = New container uploaded to AWS ECR action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high +alert.digest_mode = 1 action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -5990,9 +4681,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `container_implant_aws_detection_filter` +search = | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` [ESCU - No Windows Updates in a time frame - Rule] action.escu = 0 @@ -6000,39 +4690,29 @@ action.escu.enabled = 1 description = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. action.escu.mappings = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} action.escu.data_models = ["Updates"] -action.escu.eli5 = Keeping your systems up-to-date with the latest patches is an important step in keeping your systems secured. For Windows endpoints, Microsoft typically releases patches on the second Tuesday of every month. These patches contain fixes for vulnerabilities in the system that could potentially be exploited by malicious actors. This search checks for messages regarding Windows updates in the 'Update' data model. If a message indicating a successful update has not been observed in 60 days, a notable event will be generated. These systems should be checked to determine why it has not been updated in that time frame. +action.escu.eli5 = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. action.escu.how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-08-15 +action.escu.creation_date = 2017-09-15 action.escu.modification_date = 2017-09-15 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - No Windows Updates in a time frame - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor for Updates"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = No Windows Updates in a time frame +action.correlationsearch.label = ESCU - No Windows Updates in a time frame - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, user -action.notable.param.rule_description = The system $src$ has not generated a successful Windows Update event in 60 days or more. -action.notable.param.rule_title = No Windows updates in last 60 days on $src$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. +action.notable.param.rule_title = No Windows Updates in a time frame action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6041,16 +4721,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" +search = | tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` [ESCU - Okta Account Lockout Events - Rule] action.escu = 0 action.escu.enabled = 1 description = Detect Okta user lockout events -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} -action.escu.eli5 = This search detects when a user exceeds the maximum configured Okta login attempts and the account is subsequently locked out. This is often indicative of brtue force attempts against a user account. +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Detect Okta user lockout events action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. action.escu.known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. action.escu.creation_date = 2020-04-01 @@ -6058,31 +4738,20 @@ action.escu.modification_date = 2020-04-01 action.escu.confidence = high action.escu.full_search_name = ESCU - Okta Account Lockout Events - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Infrastructure -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Okta"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Okta Account Lockout Events +action.correlationsearch.label = ESCU - Okta Account Lockout Events - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = User $user$ locked out of Okta - too many attempts -action.notable.param.rule_title = Okta user lockout events +action.notable.param.rule_description = Detect Okta user lockout events +action.notable.param.rule_title = Okta Account Lockout Events action.notable.param.security_domain = access action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6091,16 +4760,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=okta_log displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip |`okta_user_lockouts_filter` +search = `okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` [ESCU - Okta Failed SSO Attempts - Rule] action.escu = 0 action.escu.enabled = 1 description = Detect failed Okta SSO events -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} -action.escu.eli5 = This search looks for events that indicate a user attempted to access an app they did not have permissions to access. This could indicate attempts to access prohibited applications. Please leverage the `okta_failed_sso_attempt_filter` macro to filter out false positives +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Detect failed Okta SSO events action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. action.escu.known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to. action.escu.creation_date = 2020-04-01 @@ -6108,31 +4777,20 @@ action.escu.modification_date = 2020-04-01 action.escu.confidence = high action.escu.full_search_name = ESCU - Okta Failed SSO Attempts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Infrastructure -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Okta"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Okta Failed SSO Attempts +action.correlationsearch.label = ESCU - Okta Failed SSO Attempts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = Okta user $user$ attempted to access unauthorized app -action.notable.param.rule_title = Okta user SSO attempt to unauthorized app +action.notable.param.rule_description = Detect failed Okta SSO events +action.notable.param.rule_title = Okta Failed SSO Attempts action.notable.param.security_domain = access action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6141,16 +4799,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=okta_log displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`okta_failed_sso_attempt_filter` +search = `okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` [ESCU - Okta User Logins From Multiple Cities - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects logins from the same user from different states in a 24 hour period. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} -action.escu.eli5 = This search detects users logging in from multiple states in the last 24 hours. This can be indicative of an attacker using compromised credentials to log in to Okta. The efficacy of this search is highly dependant on the mobility of the users using Okta. It is particularly useful in situations where users should explicitly *not* be travelling, such as during the COVID-19 pandemic. +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects logins from the same user from different states in a 24 hour period. action.escu.how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. action.escu.known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. action.escu.creation_date = 2020-04-01 @@ -6158,31 +4816,21 @@ action.escu.modification_date = 2020-04-01 action.escu.confidence = high action.escu.full_search_name = ESCU - Okta User Logins From Multiple Cities - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Infrastructure -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Okta"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Okta Activity"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -24h@h -dispatch.latest_time = -5m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Okta User Logins From Multiple Cities +action.correlationsearch.label = ESCU - Okta User Logins From Multiple Cities - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = User $user$ logged into Okta from different cities -action.notable.param.rule_title = Okta user login from different cities +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search detects logins from the same user from different states in a 24 hour period. +action.notable.param.rule_title = Okta User Logins From Multiple Cities action.notable.param.security_domain = access action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6191,49 +4839,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = eventtype=okta_log displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`okta_user_logins_from_multiple_cities_filter`| search locations > 1 +search = `okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 [ESCU - Open Redirect in Splunk Web - Rule] action.escu = 0 action.escu.enabled = 1 description = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} action.escu.data_models = [] -action.escu.eli5 = This search looks within Splunk's internal logs for evidence of CVE-2016-4859 open redirect exploitation attempts. +action.escu.eli5 = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. action.escu.how_to_implement = No extra steps needed to implement this search. action.escu.known_false_positives = None identified -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2017-09-19 action.escu.modification_date = 2017-09-19 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Open Redirect in Splunk Web - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Splunk Server -action.escu.fields_required = ["host"] -action.escu.entities = ["host"] -action.escu.providing_technologies = ["Splunk Enterprise"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Splunk Enterprise Vulnerability"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Open Redirect in Splunk Web +action.correlationsearch.label = ESCU - Open Redirect in Splunk Web - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = host -action.notable.param.rule_description = Search for exploitation of the Splunk Open Redirect Vulnerability +action.notable.param.rule_description = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. action.notable.param.rule_title = Open Redirect in Splunk Web action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = host -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = host -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6242,49 +4878,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = index=_internal sourcetype=splunk_web_access return_to="/%09/*" +search = index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` [ESCU - Osquery pack - ColdRoot detection - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for ColdRoot events from the osx-attacks osquery pack. -action.escu.mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Execution", "Persistence", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} -action.escu.data_models = ["Alerts"] -action.escu.eli5 = The search looks at the Alerts data model to identify those generated from the osquery osx-attacks.conf pack, which search for the ColdRoot RAT. +action.escu.mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for ColdRoot events from the osx-attacks osquery pack. action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model action.escu.known_false_positives = There are no known false positives. action.escu.creation_date = 2019-01-29 action.escu.modification_date = 2019-01-29 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Osquery pack - ColdRoot detection - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["host"] -action.escu.entities = ["host"] -action.escu.providing_technologies = ["OSquery"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["ColdRoot MacOS RAT"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Osquery pack - ColdRoot detection +action.correlationsearch.label = ESCU - Osquery pack - ColdRoot detection - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = host, user -action.notable.param.rule_description = Host $host$ generated an alert for the macOS RAT ColdRoot -action.notable.param.rule_title = Osquery ColdRoot alert for $host$ +action.notable.param.rule_description = This search looks for ColdRoot events from the osx-attacks osquery pack. +action.notable.param.rule_title = Osquery pack - ColdRoot detection action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = host -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = host -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6293,49 +4917,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path +search = | from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter` [ESCU - Overwriting Accessibility Binaries - Rule] action.escu = 0 action.escu.enabled = 1 description = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Accessibility Features"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search returns all the different accessibility binaries that have been modified for each Windows host. +action.escu.eli5 = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. -action.escu.creation_date = 2017-12-07 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high action.escu.full_search_name = ESCU - Overwriting Accessibility Binaries - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Privilege Escalation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Overwriting Accessibility Binaries +action.correlationsearch.label = ESCU - Overwriting Accessibility Binaries - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file, $file_name$, was created in the default shim database directory on $dest. -action.notable.param.rule_title = Modification to accessibility binary, $file_path$, was detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. +action.notable.param.rule_title = Overwriting Accessibility Binaries action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6344,49 +4957,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter` [ESCU - Process Execution via WMI - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes launched via WMI. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for processes launched via WMI, either remotely or locally, by looking for processes launched by WmiPrvSE.exe, which is the process WMI uses to execute new processes and commands. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for processes launched via WMI. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. -action.escu.creation_date = 2018-10-23 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Process Execution via WMI - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Process Execution via WMI +action.correlationsearch.label = ESCU - Process Execution via WMI - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for child processes of WmiPrvSE.exe, which indicates that a process was launched via WMI. -action.notable.param.rule_title = Process launched via WMI on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launched via WMI. +action.notable.param.rule_title = Process Execution via WMI action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6395,49 +4997,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `wmi_process_launch_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name = *WmiPrvSE.exe by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `process_execution_via_wmi_filter` [ESCU - Processes Tapping Keyboard Events - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input -action.escu.mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Collection"], "nist": ["DE.DP"]} -action.escu.data_models = ["Alerts"] -action.escu.eli5 = The search leverages Alerts generated from the osquery osx-attacks.conf pack search `Keyboard_Event_Taps` to detect when a process is monitoring the keystrokes of a machine, This is a common technique used by macOS remote access trojans to log keystrokes from a machine +action.escu.mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. action.escu.known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. action.escu.creation_date = 2019-01-25 action.escu.modification_date = 2019-01-25 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Processes Tapping Keyboard Events - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["host"] -action.escu.entities = ["host"] -action.escu.providing_technologies = ["OSquery"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["ColdRoot MacOS RAT"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Processes Tapping Keyboard Events +action.correlationsearch.label = ESCU - Processes Tapping Keyboard Events - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = host, cmd, process_id -action.notable.param.rule_description = Host $host$ has process $process_id$ tapping keyboard events with command $cmd$ -action.notable.param.rule_title = Host $host has process $process_id$ monitoring its keystrokes +action.notable.param.rule_description = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input +action.notable.param.rule_title = Processes Tapping Keyboard Events action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = host -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = host -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6446,49 +5036,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id +search = | from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter` [ESCU - Processes created by netsh - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for all processes with the parent process "c:\Windows\System32\netsh.exe" and returns the process, the command line used to execute it, the host name, and the user context under which it ran. +action.escu.eli5 = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. action.escu.how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. -action.escu.creation_date = 2018-01-04 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Processes created by netsh - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Netsh Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Processes created by netsh +action.correlationsearch.label = ESCU - Processes created by netsh - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process -action.notable.param.rule_description = A process, $process$, is spawned by netsh.exe. It is highly unlikely for netsh to have any child processes. -action.notable.param.rule_title = Process spawned by netsh.exe detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. +action.notable.param.rule_title = Processes created by netsh action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6497,49 +5076,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*C:\\Windows\\System32\\netsh.exe*" by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*C:\\Windows\\System32\\netsh.exe*" by Processes.user Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `processes_created_by_netsh_filter` [ESCU - Processes launching netsh - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for all the parent processes of netsh.exe and returns that process, the command-line used to execute it, the host name, and the user context under which it ran. +action.escu.eli5 = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model action.escu.known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. -action.escu.creation_date = 2017-01-08 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Processes launching netsh - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Netsh Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Netsh Abuse", "Disabling Security Tools", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Processes launching netsh +action.correlationsearch.label = ESCU - Processes launching netsh - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process, cmdline -action.notable.param.rule_description = A process detected on $dest$ is launching netsh.exe. -action.notable.param.rule_title = Process launching netsh.exe detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. +action.notable.param.rule_title = Processes launching netsh action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, parent_process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6548,49 +5116,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=netsh.exe by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=netsh.exe by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `processes_launching_netsh_filter` [ESCU - Prohibited Network Traffic Allowed - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["DE.AE", "PR.AC"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. +action.escu.eli5 = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-04-18 +action.escu.creation_date = 2017-09-11 action.escu.modification_date = 2017-09-11 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Prohibited Network Traffic Allowed +action.correlationsearch.label = ESCU - Prohibited Network Traffic Allowed - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = This search looks for network traffic defined by port and transport in the ES lookup table "lookup_interesting_ports", that is marked as prohibited, and yet has an 'allow' action in the Network_Traffic data model. This should help to identify areas where a network device is not properly configured. -action.notable.param.rule_title = Prohibited Network Traffic Allowed from $src_ip$ +action.notable.param.rule_description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +action.notable.param.rule_title = Prohibited Network Traffic Allowed action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip,src_ip -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6599,49 +5155,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` [ESCU - Prohibited Software On Endpoint - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for applications on the endpoint that you have marked as prohibited. -action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as "prohibited" in the Enterprise Security "Interesting Processes" table. +action.escu.eli5 = This search looks for applications on the endpoint that you have marked as prohibited. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-06-26 +action.escu.creation_date = 2019-10-11 action.escu.modification_date = 2019-10-11 action.escu.confidence = high action.escu.full_search_name = ESCU - Prohibited Software On Endpoint - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Monitor for Unauthorized Software", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Monitor for Unauthorized Software", "Emotet Malware DHS Report TA18-201A ", "SamSam Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Prohibited Software On Endpoint +action.correlationsearch.label = ESCU - Prohibited Software On Endpoint - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, user -action.notable.param.rule_description = Prohibited software $process_name$ has been detected on $dest$. -action.notable.param.rule_title = Prohibited Software Detected On $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for applications on the endpoint that you have marked as prohibited. +action.notable.param.rule_title = Prohibited Software On Endpoint action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6650,49 +5195,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `prohibited_softwares` | `prohibited_software_on_endpoint_filter` [ESCU - Protocol or Port Mismatch - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port"], "nist": ["DE.AE", "PR.AC"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "PR.AC"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search looks for instances in which the protocol observed is not consistent with the port and transport protocol typically used for that protocol. For example, looking for network traffic other than HTTP running over TCP port 80. Such behavior could indicate a misconfiguration or a custom command and control protocol that has been designed to look like ordinary web traffic. The search will also identify if HTTP traffic is observed running on unexpected ports. This can be common in many environments. +action.escu.eli5 = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. action.escu.how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-04-18 +action.escu.creation_date = 2017-09-11 action.escu.modification_date = 2017-09-11 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Protocol or Port Mismatch - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Protocol or Port Mismatch +action.correlationsearch.label = ESCU - Protocol or Port Mismatch - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest_ip, src_ip -action.notable.param.rule_description = This search looks for network traffic on common ports where the underlying protocol does not match the port being used. For example, this search should identify cases where protocols other than HTTP are running on port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications in traffic that is typically allowed and not well inspected. -action.notable.param.rule_title = Protocol / Port Mismatch from $src_ip$ +action.notable.param.rule_description = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. +action.notable.param.rule_title = Protocol or Port Mismatch action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip, dest_port -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6701,49 +5234,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` [ESCU - Protocols passing authentication in cleartext - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "mitre_attack": ["Credential Access", "Lateral Movement", "Collection"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext. +action.escu.eli5 = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. action.escu.how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. action.escu.known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. -action.escu.creation_date = 2017-08-03 +action.escu.creation_date = 2017-09-15 action.escu.modification_date = 2017-09-15 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Protocols passing authentication in cleartext - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Use of Cleartext Protocols"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Protocols passing authentication in cleartext +action.correlationsearch.label = ESCU - Protocols passing authentication in cleartext - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, dest, user -action.notable.param.rule_description = This search looks for the use of cleartext protocols that are known to pass authentication information in the clear. The cleartext credentials are typically passed at the beginning of the session. -action.notable.param.rule_title = Possible credential leak over cleartext protocol +action.notable.param.nes_fields = ['user', 'dest', 'src'] +action.notable.param.rule_description = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. +action.notable.param.rule_title = Protocols passing authentication in cleartext action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6752,49 +5274,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` -[ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule] +[ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for modifications to registry paths that specify the definition and configuration of Windows services by reg.exe. Reg.exe is a Windows utility that allows for manipulation of the registry via the command line. Malware often uses the Windows services architecture to persist, hide in plain sight, and gain the ability to interact with the Windows kernel. While it is common to modify the configuration of Windows services (and new services may be created with software installs), the use of reg.exe to create or modify a service configuration is unusual and a technique commonly used by attackers. The search returns the count, the first time the activity was seen, the last time activity was seen, the registry path that was modified, the host where the modification took place, and the user that performed the modification. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. action.escu.known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. -action.escu.creation_date = 2018-6-29 +action.escu.creation_date = 2019-03-01 action.escu.modification_date = 2019-03-01 action.escu.confidence = high -action.escu.full_search_name = ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule +action.escu.full_search_name = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Service Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Service Abuse", "Windows Persistence Techniques"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Reg.exe Manipulating Windows Services Registry Keys +action.correlationsearch.label = ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process -action.notable.param.rule_description = A registry key associated with Windows services was modified via reg.exe on $dest$ by $src_user$. -action.notable.param.rule_title = Modification of Windows Services Via Reg.exe on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. +action.notable.param.rule_title = Reg exe Manipulating Windows Services Registry Keys action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6803,49 +5314,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\services\\*" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path] +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\services\\*" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path] | `reg_exe_manipulating_windows_services_registry_keys_filter` -[ESCU - Reg.exe used to hide files/directories via registry keys - Rule] +[ESCU - Reg exe used to hide files directories via registry keys - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for command-line arguments used to hide a file or directory using the reg add command. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory. +action.escu.eli5 = The search looks for command-line arguments used to hide a file or directory using the reg add command. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None at the moment -action.escu.creation_date = 2017-10-27 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Reg.exe used to hide files/directories via registry keys - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Reg exe used to hide files directories via registry keys - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys +action.correlationsearch.label = ESCU - Reg exe used to hide files directories via registry keys - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process -action.notable.param.rule_description = Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory -action.notable.param.rule_title = Regedit.exe used to hide a file/directory on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search looks for command-line arguments used to hide a file or directory using the reg add command. +action.notable.param.rule_title = Reg exe used to hide files directories via registry keys action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6854,49 +5354,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` [ESCU - Registry Keys Used For Persistence - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Registry Run Keys / Start Folder", "AppInit DLLs", "Authentication Package"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for specific registry paths that malware often uses to ensure survivability and persistence on system startup. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. action.escu.known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. -action.escu.creation_date = 2017-08-23 +action.escu.creation_date = 2017-10-10 action.escu.modification_date = 2017-10-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Registry Keys Used For Persistence - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Suspicious MSHTA Activity", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Windows Persistence Techniques", "Emotet Malware DHS Report TA18-201A "] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Registry Keys Used For Persistence +action.correlationsearch.label = ESCU - Registry Keys Used For Persistence - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, registry_path -action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $src_user$. -action.notable.param.rule_title = Registry Key Associated With Persistence Modified on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. +action.notable.param.rule_title = Registry Keys Used For Persistence action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,registry_path -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6905,49 +5394,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest , Registry.status, Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest , Registry.status, Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` [ESCU - Registry Keys Used For Privilege Escalation - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence", "Accessibility Features"], "mitre_technique_id": ["T1183"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for specific registry paths that malware often uses to elevate privileges. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place, and the user who performed the modification. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. action.escu.known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. -action.escu.creation_date = 2017-12-07 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Registry Keys Used For Privilege Escalation - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Privilege Escalation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Privilege Escalation", "Suspicious Windows Registry Activities"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Registry Keys Used For Privilege Escalation +action.correlationsearch.label = ESCU - Registry Keys Used For Privilege Escalation - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, registry_path -action.notable.param.rule_description = A registry key used for privilege escalation was modified on $dest$ by $user$. -action.notable.param.rule_title = Registry Key Associated With Privilege Escalation Modified on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. +action.notable.param.rule_title = Registry Keys Used For Privilege Escalation action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user, registry_path -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -6956,49 +5434,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` [ESCU - Registry Keys for Creating SHIM Databases - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Change_Analysis"] -action.escu.eli5 = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. action.escu.how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -action.escu.creation_date = 2017-08-27 +action.escu.creation_date = 2017-09-15 action.escu.modification_date = 2017-09-15 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Registry Keys for Creating SHIM Databases +action.correlationsearch.label = ESCU - Registry Keys for Creating SHIM Databases - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $user$ -action.notable.param.rule_title = Registry Key Associated With SHIM databases on $dest$ +action.notable.param.rule_description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. +action.notable.param.rule_title = Registry Keys for Creating SHIM Databases action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,object_path -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7007,49 +5473,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` | `registry_keys_for_creating_shim_databases_filter` [ESCU - Remote Desktop Network Bruteforce - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. -action.escu.mappings = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. +action.escu.eli5 = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. action.escu.creation_date = 2018-12-14 action.escu.modification_date = 2018-12-14 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Bruteforce +action.correlationsearch.label = ESCU - Remote Desktop Network Bruteforce - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote-desktop traffic detected from $src$ to $dest$. This activity is consistent with a brute-force attack. -action.notable.param.rule_title = Bruteforce Remote Desktop Network Traffic detected from $src$ to $dest$ +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. +action.notable.param.rule_title = Remote Desktop Network Bruteforce action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7058,49 +5513,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` [ESCU - Remote Desktop Network Traffic - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. +action.escu.eli5 = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-10 action.escu.modification_date = 2020-01-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["SamSam Ransomware", "Hidden Cobra Malware", "Lateral Movement"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Traffic +action.correlationsearch.label = ESCU - Remote Desktop Network Traffic - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote Desktop Traffic detected between $src$ and $dest$. These two systems typically do not communicate with RDP -action.notable.param.rule_title = Uncommon Remote Desktop Network Traffic between $src$ and $dest$ +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. +action.notable.param.rule_title = Remote Desktop Network Traffic action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7109,7 +5553,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` @@ -7117,41 +5560,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the "common_rdp_source" category applied. Categories are applied to systems using the Assets and Identity framework. +action.escu.eli5 = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-13 action.escu.modification_date = 2020-01-13 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Desktop Process Running On System - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Process Running On System +action.correlationsearch.label = ESCU - Remote Desktop Process Running On System - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = The system $dest$ is running the remote desktop process, mstsc.exe. This system does not commonly run this application. -action.notable.param.rule_title = Remote Desktop Process Running On $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. +action.notable.param.rule_title = Remote Desktop Process Running On System action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7160,7 +5593,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter` @@ -7168,41 +5600,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers are increasingly abusing native Windows utilities such as wmic.exe as a means to "live off the land", and avoid introducing new executables to the target system. In this search, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +action.escu.eli5 = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. -action.escu.creation_date = 2017-01-13 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Process Instantiation via WMI - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Process Instantiation via WMI +action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. -action.notable.param.rule_title = Remote process instantiation via WMI on $dest$ +action.notable.param.rule_title = Remote Process Instantiation via WMI action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7211,49 +5633,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` [ESCU - Remote Registry Key modifications - Rule] action.escu = 0 action.escu.enabled = 1 description = This search monitors for remote modifications to registry keys. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence", "Lateral Movement"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for modifications made to the Windows registry from remote locations using reg.exe—a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search monitors for remote modifications to registry keys. action.escu.how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. -action.escu.creation_date = 2018-05-31 +action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote Registry Key modifications - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Registry Key modifications +action.correlationsearch.label = ESCU - Remote Registry Key modifications - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = A registry key was modified remotely using the machine $dest$ by $user$. -action.notable.param.rule_title = Remote Registry Key Modification detection on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search monitors for remote modifications to registry keys. +action.notable.param.rule_title = Remote Registry Key modifications action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user,registry_path -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7262,49 +5673,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` [ESCU - Remote WMI Command Attempt - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for wmic.exe being launched with parameters to operate on remote systems. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +action.escu.eli5 = This search looks for wmic.exe being launched with parameters to operate on remote systems. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. -action.escu.creation_date = 2017-01-13 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 50 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote WMI Command Attempt +action.correlationsearch.label = ESCU - Remote WMI Command Attempt - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest,user,process_name +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to operate on remote systems. -action.notable.param.rule_title = Endpoint - Remote WMI command attempt +action.notable.param.rule_title = Remote WMI Command Attempt action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process_name -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7313,49 +5713,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter` [ESCU - RunDLL Loading DLL By Ordinal - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Execution", "Rundll32"], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1085"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for rundll32.exe being run, loading a DLL out of a directory or subdirectory of AppData, and specifying the function at ordinal 2 be run. +action.escu.eli5 = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -action.escu.creation_date = 2016-08-09 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - RunDLL Loading DLL By Ordinal - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Unusual Processes"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = RunDLL Loading DLL By Ordinal +action.correlationsearch.label = ESCU - RunDLL Loading DLL By Ordinal - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions with no names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. -action.notable.param.rule_title = Endpoint - Suspicious RunDLL usage +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. +action.notable.param.rule_title = RunDLL Loading DLL By Ordinal action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7364,49 +5753,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe Processes.process="*AppData*" Processes.process="*,#2" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe Processes.process="*AppData*" Processes.process="*,#2" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll_loading_dll_by_ordinal_filter` [ESCU - SMB Traffic Spike - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. +action.escu.eli5 = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -action.escu.creation_date = 2017-08-20 +action.escu.creation_date = 2020-01-22 action.escu.modification_date = 2020-01-22 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -7d@d -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = SMB Traffic Spike +action.correlationsearch.label = ESCU - SMB Traffic Spike - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = There was a spike in SMB traffic from $src$. -action.notable.param.rule_title = SMB Traffic Spike from $src$ +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. +action.notable.param.rule_title = SMB Traffic Spike action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7415,7 +5793,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` @@ -7423,44 +5800,34 @@ search = | tstats `security_content_summariesonly` count from datamodel=Network_ action.escu = 0 action.escu.enabled = 1 description = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.eli5 = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -action.escu.creation_date = 2019-05-08 +action.escu.creation_date = 2020-01-22 action.escu.modification_date = 2020-01-22 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - SMB Traffic Spike - MLTK - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = SMB Traffic Spike - MLTK +action.correlationsearch.label = ESCU - SMB Traffic Spike - MLTK - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = There was a spike in SMB traffic from $src$ -action.notable.param.rule_title = SMB Traffic Spike from $src$ +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. +action.notable.param.rule_title = SMB Traffic Spike - MLTK action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7469,49 +5836,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike_mltk_filter` +search = | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` [ESCU - SQL Injection with Long URLs - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for long URLs that have several SQL commands visible within them. -action.escu.mappings = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Commonly Used Port"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} action.escu.data_models = ["Web"] -action.escu.eli5 = This search looks only at your web servers and returns the source, the web server, the URL and its length, and the user agent associated with HTTP GET requests for extremely long URLs or user agent lengths with more than three common SQL commands found within the URL. +action.escu.eli5 = This search looks for long URLs that have several SQL commands visible within them. action.escu.how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. action.escu.known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2017-09-19 action.escu.modification_date = 2017-09-19 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - SQL Injection with Long URLs - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Database Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["SQL Injection"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = SQL Injection with Long URLs +action.correlationsearch.label = ESCU - SQL Injection with Long URLs - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, src, url -action.notable.param.rule_description = Using the length of url or user agent to identify SQL injection +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for long URLs that have several SQL commands visible within them. action.notable.param.rule_title = SQL Injection with Long URLs action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src,url -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7520,17 +5876,16 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 +search = | tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` [ESCU - Samsam Test File Write - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at file modifications across your hosts and monitors for a file named "test.txt" written to "windows\system32". This file is copied to potential targets during SamSam ransomware attacks to test the attacker's ability to access remote systems. If the file is successfully copied to the system, the system is added to a list of targets on which to deploy ransomware. +action.escu.eli5 = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = No false positives have been identified. action.escu.creation_date = 2018-12-14 @@ -7538,31 +5893,21 @@ action.escu.modification_date = 2018-12-14 action.escu.confidence = high action.escu.full_search_name = ESCU - Samsam Test File Write - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Samsam Test File Write +action.correlationsearch.label = ESCU - Samsam Test File Write - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file named "test.txt," which is indicative of a SamSam ransomware attack, was written to system32 on $dest$. -action.notable.param.rule_title = File consistent with SamSam probes detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. +action.notable.param.rule_title = Samsam Test File Write action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7571,49 +5916,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter` -[ESCU - Sc.exe Manipulating Windows Services - Rule] +[ESCU - Sc exe Manipulating Windows Services - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the execution of sc.exe with parameters that indicate the utility is being used to create a new Windows service, or modify an existing one. Attackers often create a new service to host their malicious code, or they may take a non-critical service or one that is disabled, and modify it to point to their malware and enable the service if necessary. It is unusual for a service to be created or modified using the sc.exe utility. +action.escu.eli5 = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. -action.escu.creation_date = 2017-11-03 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Sc.exe Manipulating Windows Services - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Sc exe Manipulating Windows Services - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Orangeworm Attack Group", "Windows Persistence Techniques", "Windows Service Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Sc.exe Manipulating Windows Services +action.correlationsearch.label = ESCU - Sc exe Manipulating Windows Services - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. -action.notable.param.rule_title = Sc.exe Manipulating Windows Services on $dest$ +action.notable.param.rule_title = Sc exe Manipulating Windows Services action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7622,49 +5956,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` [ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Scheduled Task"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate that a specific task "reset," whose name is associated with the Dragonfly threat actor--has been created or deleted. Schtasks.exe is a native Windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = No known false positives -action.escu.creation_date = 2018-03-19 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Scheduled Task Name Used by Dragonfly Threat Actors +action.correlationsearch.label = ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command line that indicate that a task--whose name is associated with the Dragonfly threat actor--has been created or deleted -action.notable.param.rule_title = Scheduled task used by Dragonfly threat actor detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. +action.notable.param.rule_title = Scheduled Task Name Used by Dragonfly Threat Actors action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7673,49 +5996,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search (process=*delete* OR process=*create*) process=*reset* | `dragonfly_schtasks_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search (process=*delete* OR process=*create*) process=*reset* | `scheduled_task_name_used_by_dragonfly_threat_actors_filter` [ESCU - Scheduled tasks used in BadRabbit ransomware - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate that specific task names related to the Bad Rabbit ransomware were created or deleted. The specific task name used are rhaegal, drogon and viserion_. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = No known false positives -action.escu.creation_date = 2017-11-03 +action.escu.creation_date = 2019-02-28 action.escu.modification_date = 2019-02-28 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Scheduled tasks used in BadRabbit ransomware - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Scheduled tasks used in BadRabbit ransomware +action.correlationsearch.label = ESCU - Scheduled tasks used in BadRabbit ransomware - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names specific to Bad Rabbit ransomware has been created or deleted -action.notable.param.rule_title = Scheduled tasks used in BadRabbit ransomware detected on $dest$ +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. +action.notable.param.rule_title = Scheduled tasks used in BadRabbit ransomware action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7724,49 +6036,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` [ESCU - Schtasks scheduling job on remote system - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Execution", "Scheduled Task"], "mitre_technique_id": ["T1053"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. +action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2020-01-10 action.escu.modification_date = 2020-01-10 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Lateral Movement"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Schtasks scheduling job on remote system +action.correlationsearch.label = ESCU - Schtasks scheduling job on remote system - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. action.notable.param.rule_title = Schtasks scheduling job on remote system action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7775,7 +6076,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" Processes.process="* /s *" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` @@ -7783,41 +6083,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. +action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. action.escu.how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -action.escu.creation_date = 2017-11-03 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Schtasks used for forcing a reboot - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -5h@h -dispatch.latest_time = -1h@h +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Schtasks used for forcing a reboot +action.correlationsearch.label = ESCU - Schtasks used for forcing a reboot - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is scheduled to force a reboot -action.notable.param.rule_title = Schtasks used for scheduling a force reboot +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. +action.notable.param.rule_title = Schtasks used for forcing a reboot action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7826,49 +6116,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*shutdown*" Processes.process="*/r*" Processes.process="*/f*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*shutdown*" Processes.process="*/r*" Processes.process="*/f*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` [ESCU - Script Execution via WMI - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for scripts launched via WMI. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for scripts launched via WMI, either remotely or locally, by looking for the execution of scrcons.exe, which is the scripting host used by WMI, similar to wscript or cscript. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for scripts launched via WMI. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. -action.escu.creation_date = 2018-10-23 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Script Execution via WMI - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Script Execution via WMI +action.correlationsearch.label = ESCU - Script Execution via WMI - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for scrcons.exe, which indicates that a script was launched via WMI. -action.notable.param.rule_title = Script execution via WMI on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for scripts launched via WMI. +action.notable.param.rule_title = Script Execution via WMI action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7877,49 +6156,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `wmi_script_execution_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = "scrcons.exe" by Processes.user Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `script_execution_via_wmi_filter` [ESCU - Shim Database File Creation - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for files being created in `Windows\AppPatch\Custom and Windows\AppPatch\Custom64`, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -action.escu.creation_date = 2017-10-03 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 action.escu.confidence = high action.escu.full_search_name = ESCU - Shim Database File Creation - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Shim Database File Creation +action.correlationsearch.label = ESCU - Shim Database File Creation - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file, $file_name$, was created in the default shim database directory on $dest. -action.notable.param.rule_title = Shim database file created on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. +action.notable.param.rule_title = Shim Database File Creation action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7928,49 +6196,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\AppPatch\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` +search = | tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\AppPatch\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter` [ESCU - Shim Database Installation With Suspicious Parameters - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. +action.escu.eli5 = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-10-03 +action.escu.creation_date = 2019-03-01 action.escu.modification_date = 2019-03-01 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Shim Database Installation With Suspicious Parameters - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Persistence Techniques"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Shim Database Installation With Suspicious Parameters +action.correlationsearch.label = ESCU - Shim Database Installation With Suspicious Parameters - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = The system $dest$ had a shim database installed. -action.notable.param.rule_title = Shim Database Installation on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. +action.notable.param.rule_title = Shim Database Installation With Suspicious Parameters action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -7979,49 +6236,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe Processes.process="*-p*" Processes.process="*-q*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe Processes.process="*-p*" Processes.process="*-q*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter` [ESCU - Short Lived Windows Accounts - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects accounts that were created and deleted in a short time period. -action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} action.escu.data_models = ["Change"] -action.escu.eli5 = This search looks for Windows Event Logs 4720 (account creation) and 4726 (account deletion) and determines if they happen for the same user within 4 hours of each other. It will report the user and machine that reported the events and the time it first and last saw this activity. +action.escu.eli5 = This search detects accounts that were created and deleted in a short time period. action.escu.how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ action.escu.known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. action.escu.creation_date = 2018-01-05 action.escu.modification_date = 2018-01-05 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Short Lived Windows Accounts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Account Monitoring and Controls"] -cron_schedule = 0 0,4,8,12,16,20 * * * -dispatch.earliest_time = -245m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Short Lived Windows Accounts +action.correlationsearch.label = ESCU - Short Lived Windows Accounts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = The account $user$ was created and deleted in a short amount of time. -action.notable.param.rule_title = Short lived account $user$ on $dest +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search detects accounts that were created and deleted in a short time period. +action.notable.param.rule_title = Short Lived Windows Accounts action.notable.param.security_domain = access -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8030,49 +6276,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id +search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` [ESCU - Single Letter Process On Endpoint - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for process names that consist only of a single letter. -action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search returns all the processes for each endpoint and user and filters out any process that isn't 5 characters long and ends with .exe. +action.escu.eli5 = This search looks for process names that consist only of a single letter. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. -action.escu.creation_date = 2018-03-22 +action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 action.escu.confidence = high action.escu.full_search_name = ESCU - Single Letter Process On Endpoint - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["DHS Report TA18-074A"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Single Letter Process On Endpoint +action.correlationsearch.label = ESCU - Single Letter Process On Endpoint - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = A process with a single letter, $process_name$ was detected on $dest$ -action.notable.param.rule_title = Single-letter executable $process_name$ on $dest$. +action.notable.param.rule_description = This search looks for process names that consist only of a single letter. +action.notable.param.rule_title = Single Letter Process On Endpoint action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8081,9 +6315,8 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` [ESCU - Spectre and Meltdown Vulnerable Systems - Rule] action.escu = 0 @@ -8091,39 +6324,28 @@ action.escu.enabled = 1 description = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. action.escu.mappings = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} action.escu.data_models = ["Vulnerabilities"] -action.escu.eli5 = This search looks for the three CVEs associated with the Spectre and Meltdown vulnerabilities. +action.escu.eli5 = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. action.escu.how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. action.escu.known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied. -action.escu.creation_date = 2018-01-07 +action.escu.creation_date = 2017-01-07 action.escu.modification_date = 2017-01-07 action.escu.confidence = high action.escu.full_search_name = ESCU - Spectre and Meltdown Vulnerable Systems - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Nessus", "Qualys"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] -cron_schedule = 0 6 * * * -dispatch.earliest_time = -25h@h -dispatch.latest_time = -1h@h +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Spectre and Meltdown Vulnerable Systems +action.correlationsearch.label = ESCU - Spectre and Meltdown Vulnerable Systems - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = $dest is vulnerable to the Spectre or Meltdown CPU vulnerabilities. -action.notable.param.rule_title = $dest is vulnerable to the Spectre or Meltdown CPU vulnerabilities +action.notable.param.rule_description = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. +action.notable.param.rule_title = Spectre and Meltdown Vulnerable Systems action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 100 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8132,49 +6354,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` [ESCU - Spike in File Writes - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for a sharp increase in the number of files written to a particular host -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search calculates counts the number of file modification events per hour per host in your environment. It then takes the average and standard deviations of those numbers and displays any hosts with more than 20 events that have over four times the standard deviation more than the average number of file modifications. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for a sharp increase in the number of files written to a particular host action.escu.how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response products, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. action.escu.known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. -action.escu.creation_date = 2017-08-20 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = low +action.escu.confidence = high action.escu.full_search_name = ESCU - Spike in File Writes - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -7d@d -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Spike in File Writes +action.correlationsearch.label = ESCU - Spike in File Writes - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = A sharp increase in file writes was detected on $dest -action.notable.param.rule_title = Spike in file writes on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search looks for a sharp increase in the number of files written to a particular host +action.notable.param.rule_title = Spike in File Writes action.notable.param.security_domain = endpoint -action.notable.param.severity = low -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 7200s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8183,48 +6394,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `file_write_spikes_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` [ESCU - Splunk Enterprise Information Disclosure - Rule] action.escu = 0 action.escu.enabled = 1 description = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} -action.escu.eli5 = This search searches Splunk's internal logs for evidence of CVE-2018-11409 exploitation attempts. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. action.escu.how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. action.escu.known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. action.escu.creation_date = 2018-06-14 action.escu.modification_date = 2018-06-14 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Splunk Enterprise Information Disclosure - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Splunk Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Enterprise"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Splunk Enterprise Information Disclosure +action.correlationsearch.label = ESCU - Splunk Enterprise Information Disclosure - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, src_ip -action.notable.param.rule_description = The Splunk Server $dest$ had a possible Splunk information-disclosure possibility from $src_ip$ -action.notable.param.rule_title = Possible Splunk Information Disclosure Exploitation Attempt from $src_ip$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. +action.notable.param.rule_title = Splunk Enterprise Information Disclosure action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, src_ip -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8233,49 +6434,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` [ESCU - Suspicious Changes to File Associations - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for changes made to the registry that control Windows file associations. It is typical for users to change the file association to open certain types of files with specific applications. However, when these changes are legitimately performed, they are typically done via the processes explorer.exe or openwith.exe. The search first executes the subsearch that looks at the Registry node, which specifies setting a value in the registry and creates a table of process_id and dest. It then uses those arguments to find out what process and parent process were responsible for making those registry changes. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. action.escu.known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. -action.escu.creation_date = 2018-01-26 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Changes to File Associations - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Changes to File Associations +action.correlationsearch.label = ESCU - Suspicious Changes to File Associations - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name, process -action.notable.param.rule_description = The system $dest$ had an unusual change to a file association -action.notable.param.rule_title = Suspicious File Association Change on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. +action.notable.param.rule_title = Suspicious Changes to File Associations action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8284,49 +6474,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `change_file_association_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count FROM datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` [ESCU - Suspicious Email - UBA Anomaly - Rule] action.escu = 0 action.escu.enabled = 1 description = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). -action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["UEBA"] -action.escu.eli5 = This detection monitors for emails that are suspicious because of their sender, domain rareness, or behavior differences, as determined by Splunk UBA. In this search, we query the "UEBA" data model to look for anomalies that are raised by the "SuspiciousEmailDetectionModel" and will output the count, description of the anomaly, signature, the type of event in UBA, the severity, and the user who received a potentially suspicious email from a newly seen domain. It will also output all the categories associated with that anomaly. +action.escu.eli5 = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). action.escu.how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. action.escu.known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and whitelist the URL if you determine that it is a legitimate sender. action.escu.creation_date = 2019-07-21 action.escu.modification_date = 2019-07-21 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Email - UBA Anomaly - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["user", "url"] -action.escu.entities = ["user", "url"] -action.escu.providing_technologies = ["Microsoft Exchange"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious Emails"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Email - UBA Anomaly +action.correlationsearch.label = ESCU - Suspicious Email - UBA Anomaly - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, url -action.notable.param.rule_description = Newly observed email domain $url$ sent an email to $user$. -action.notable.param.rule_title = Suspicious Email Anomaly +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). +action.notable.param.rule_title = Suspicious Email - UBA Anomaly action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8335,51 +6514,39 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter` [ESCU - Suspicious Email Attachment Extensions - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for emails that have attachments with suspicious file extensions. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Execution", "Defense Evasion"], "mitre_technique_id": ["T1193"], "nist": ["DE.AE", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} action.escu.data_models = ["Email"] -action.escu.eli5 = This search looks at any email messages with attachments and checks the file names of those attachments against an included lookup file to see if it has a suspicious file extension. +action.escu.eli5 = This search looks for emails that have attachments with suspicious file extensions. action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-04-20 +action.escu.creation_date = 2020-01-27 action.escu.modification_date = 2020-01-27 action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Email Attachment Extensions - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_user", "message_id"] -action.escu.entities = ["src_user", "message_id"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Suspicious Emails"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Suspicious Emails"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Email Attachment Extensions +action.correlationsearch.label = ESCU - Suspicious Email Attachment Extensions - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user, file_name -action.notable.param.rule_description = The sender $src_user$ has sent an email with a suspicious file named $file_name$ -action.notable.param.rule_title = Suspicious Email Attachment from $src_user$ +action.notable.param.rule_description = This search looks for emails that have attachments with suspicious file extensions. +action.notable.param.rule_title = Suspicious Email Attachment Extensions action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user,message_id -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8388,49 +6555,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachments_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` [ESCU - Suspicious File Write - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for files created with names that have been linked to malicious activity. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks at files being created or modified in the Endpoint file-system data model. The names of those files are checked against an included lookup file, which contains the names of files associated with malware or attack activity. The search returns any files with matching names, along with a note (also specified in the lookup file) that gives or points to more information about the files. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for files created with names that have been linked to malicious activity. action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. action.escu.known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. -action.escu.creation_date = 2018-06-14 +action.escu.creation_date = 2019-04-25 action.escu.modification_date = 2019-04-25 action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious File Write - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Hidden Cobra Malware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious File Write +action.correlationsearch.label = ESCU - Suspicious File Write - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A write to a filename associated with malicious activity detected on $dest$. -action.notable.param.rule_title = Suspicious File Write Detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search looks for files created with names that have been linked to malicious activity. +action.notable.param.rule_title = Suspicious File Write action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8439,48 +6595,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` +search = | tstats `security_content_summariesonly` count values(Filesystem.action) as action values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `suspicious_writes` | `suspicious_file_write_filter` [ESCU - Suspicious Java Classes - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. -action.escu.mappings = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution"], "nist": ["DE.AE"]} -action.escu.eli5 = The search leverages HTTP form data from typically POST events that can be captured with Splunk streams or similar wire data capture tools. The search looks for java classes like `processbuilder` and `runtime` are used to create a new process and execute commands inside java, and are synonymous with spawning a shell. There are very exceptional reasons to ever these classes in Java via an HTTP API and hence when seen are highly suspicious. Also, this is a common vectors leverage to exploit Apache Struts. +action.escu.mappings = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. action.escu.known_false_positives = There are no known false positives. action.escu.creation_date = 2018-12-06 action.escu.modification_date = 2018-12-06 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious Java Classes - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Java Classes +action.correlationsearch.label = ESCU - Suspicious Java Classes - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src, url, http_user_agent -action.notable.param.rule_description = The host $src$ with user agent $http_user_agent$ is sending web traffic to $url$, which contains suspicious Java classes. These classes may be indicative of remote code execution in Java frameworks, such as Apache Struts. -action.notable.param.rule_title = Suspicious Java Classes: Possible RCE against Struts or similar Java framework from $src$ +action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.rule_description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. +action.notable.param.rule_title = Suspicious Java Classes action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src, url, http_user_agent -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8489,49 +6635,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="stream:http" http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = `stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter` [ESCU - Suspicious LNK file launching a process - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. -action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = In this search, we are essentially trying to detect if a LNK file created under the C:\User* or *\Local\Temp\* directory structures is launching a process with in 1 hour of its creation. LNK files or also known as Windows shortcut files are commonly associated with phishing and are a [preferred method used for exploitation](https://www.fireeye.com/blog/threat-research/2017/04/fin7-phishing-lnk.html). +action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. action.escu.how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. action.escu.known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to execute process from temporary or user directories. -action.escu.creation_date = 2019-04-29 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious LNK file launching a process - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Phishing Payloads"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious LNK file launching a process +action.correlationsearch.label = ESCU - Suspicious LNK file launching a process - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, file_name -action.notable.param.rule_description = suspicious LNK file from $file_name$ is executing a process $process_name$ on $dest$ -action.notable.param.rule_title = LNK file $file_name$ is executing process $process_name$ on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. +action.notable.param.rule_title = Suspicious LNK file launching a process action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8540,49 +6675,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND (Filesystem.file_path="C:\\Users*" OR Filesystem.file_path="*Local\\Temp*") by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `lnk_executing_a_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND (Filesystem.file_path="C:\\Users*" OR Filesystem.file_path="*Local\\Temp*") by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `suspicious_lnk_file_launching_a_process_filter` -[ESCU - Suspicious Reg.exe Process - Rule] +[ESCU - Suspicious Reg exe Process - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry", "Disabling Security Tools"], "nist": ["DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the execution of reg.exe with a parent process of cmd.exe. It then executes a subsearch looking for those cmd.exe processes with a parent that is not explorer.exe. It then joins those two searches to make sure that the reg.exe process is a grandchild of the non explorer.exe process. The search will return the number of such instances and the first and last time this activity has been seen on each endpoint and user. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112", "T1089"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. -action.escu.creation_date = 2017-10-11 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium -action.escu.full_search_name = ESCU - Suspicious Reg.exe Process - Rule +action.escu.confidence = high +action.escu.full_search_name = ESCU - Suspicious Reg exe Process - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Windows Defense Evasion Tactics"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Reg.exe Process +action.correlationsearch.label = ESCU - Suspicious Reg exe Process - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ had reg.exe process run not initiated by a user. -action.notable.param.rule_title = Suspicious reg.exe process detected on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. +action.notable.param.rule_title = Suspicious Reg exe Process action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8591,51 +6715,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` [ESCU - Suspicious wevtutil Usage - Rule] action.escu = 0 action.escu.enabled = 1 description = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for execution of wevtutil.exe with command-line arguments that indicate that it has been used to delete the setup, application, security, or system event logs. The search returns the number of times the behavior was observed, the first and last time it was seen, the host exhibiting the behavior and the user context of the process execution. +action.escu.eli5 = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -action.escu.creation_date = 2017-02-17 +action.escu.creation_date = 2019-02-28 action.escu.modification_date = 2019-02-28 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious wevtutil Usage - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious wevtutil Usage +action.correlationsearch.label = ESCU - Suspicious wevtutil Usage - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = wevtutil is the windows event log tool. This searches for wevtutil clearing the security or system logs. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. action.notable.param.rule_title = Suspicious wevtutil Usage action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.drilldown_name = View wevtutil process information on $dest$ -action.notable.param.drilldown_search = | from datamodel:Endpoint.Processes | search dest="$dest$" process=*wevtutil* -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8644,48 +6755,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` [ESCU - Suspicious writes to System Volume Information - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects writes to the 'System Volume Information' folder by something other than the System process. -action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} -action.escu.eli5 = This search uses data on file writes captured via Sysmon to watch for writes to the "System Volume Information" folder by processes other than the system process. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. It then looks for a file created with a path that includes "System Volume Information" and a process ID (PID) other than 4. PID 4 is assigned to the System process on Windows systems. Excluding these writes allows us to filter out legitimate activity. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects writes to the 'System Volume Information' folder by something other than the System process. action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. action.escu.creation_date = 2018-01-08 action.escu.modification_date = 2018-01-08 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious writes to System Volume Information - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious writes to System Volume Information +action.correlationsearch.label = ESCU - Suspicious writes to System Volume Information - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name, process -action.notable.param.rule_description = The process $process$ on $dest$ wrote $file_name$ to 'System Volume Information'. -action.notable.param.rule_title = Suspicious process $process$ wrote to 'System Volume Information' on $dest$ +action.notable.param.rule_description = This search detects writes to the 'System Volume Information' folder by something other than the System process. +action.notable.param.rule_title = Suspicious writes to System Volume Information action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8694,49 +6794,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = (`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume\ Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_writes_to_system_volume_information_filter` [ESCU - Suspicious writes to windows Recycle Bin - Rule] action.escu = 0 action.escu.enabled = 1 description = This search detects writes to the recycle bin by a process other than explorer.exe. -action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects writes to the recycle bin by a process other than explorer.exe. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. action.escu.known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -action.escu.creation_date = 2018-01-08 +action.escu.creation_date = 2020-02-03 action.escu.modification_date = 2020-02-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Suspicious writes to windows Recycle Bin - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Collection and Staging"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious writes to windows Recycle Bin +action.correlationsearch.label = ESCU - Suspicious writes to windows Recycle Bin - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name, process_name -action.notable.param.rule_description = The process $process_name$ on $dest$ wrote $file_name$ to the Recycle Bin. -action.notable.param.rule_title = Suspicious process $process_name$ wrote to the Recycle Bin on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search detects writes to the recycle bin by a process other than explorer.exe. +action.notable.param.rule_title = Suspicious writes to windows Recycle Bin action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8745,49 +6834,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` [ESCU - System Processes Run From Unexpected Locations - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Masquerading"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search returns all the processes that are not executing out of the C:\Windows\System32 or C:\Windows\SysWOW64 directories. Next, it takes the filename and looks it up in a table `is_windows_system_file` of files that should normally run out of the C:\Windows\System32 or C:\Windows\SysWOW64 directory. Any matches are then returned. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. action.escu.how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. action.escu.known_false_positives = None identified -action.escu.creation_date = 2016-08-24 +action.escu.creation_date = 2020-02-04 action.escu.modification_date = 2020-02-04 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - System Processes Run From Unexpected Locations - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Suspicious Command-Line Executions", "Unusual Processes"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = System Processes Run From Unexpected Locations +action.correlationsearch.label = ESCU - System Processes Run From Unexpected Locations - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user, process_name, dest -action.notable.param.rule_description = The system $dest$ has a process that normally runs out of Windows\System32\ that is not being run from that location. -action.notable.param.rule_title = System Processes Run From Unexpected Location on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. +action.notable.param.rule_title = System Processes Run From Unexpected Locations action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process_name -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8796,49 +6874,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.user Processes.dest Processes.process_name Processes.process_id Processes.process_path Processes.parent_process_name Processes.process_hash| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` [ESCU - TOR Traffic - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. -action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration"], "nist": ["DE.AE"]} +action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = The search leverages the Enterprise Security Network_Traffic data model to look for network traffic that has been identified as TOR and marked as 'allowed'. +action.escu.eli5 = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. action.escu.known_false_positives = None at this time -action.escu.creation_date = 2017-08-21 +action.escu.creation_date = 2017-09-11 action.escu.modification_date = 2017-09-11 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - TOR Traffic - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = TOR Traffic +action.correlationsearch.label = ESCU - TOR Traffic - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = Network traffic accessing TOR detected from $src_ip$ -action.notable.param.rule_title = TOR Network Traffic Allowed from $src_ip$ +action.notable.param.rule_description = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. +action.notable.param.rule_title = TOR Traffic action.notable.param.security_domain = network -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8847,49 +6913,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` [ESCU - USN Journal Deletion - Rule] action.escu = 0 action.escu.enabled = 1 description = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. -action.escu.mappings = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the execution of fsutil.exe with command-line arguments to delete the USN journal. The search returns the count of the number of times it's seen this process execution with these arguments, the first and last time it's seen this behavior, the hosts it was executed on, and the user context under which it was executed. +action.escu.eli5 = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-06-27 +action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - USN Journal Deletion - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = USN Journal Deletion +action.correlationsearch.label = ESCU - USN Journal Deletion - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ deleted its NTFS journals. -action.notable.param.rule_title = File System Journal Deleted on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. +action.notable.param.rule_title = USN Journal Deletion action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process_name -alert.suppress.period = 14400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8898,49 +6953,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` [ESCU - Uncommon Processes On Endpoint - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for applications on the endpoint that you have marked as uncommon. -action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Accessibility Features"], "nist": ["ID.AM", "PR.DS"]} +action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search returns the number of times, as well as the first and last time, it has seen every process run for each endpoint and user, and then displays only those processes that you have marked as uncommon in the `uncommon_processes_default.csv` table. +action.escu.eli5 = This search looks for applications on the endpoint that you have marked as uncommon. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-12-08 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 action.escu.confidence = high action.escu.full_search_name = ESCU - Uncommon Processes On Endpoint - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Unusual Processes", "Windows Privilege Escalation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Uncommon Processes On Endpoint +action.correlationsearch.label = ESCU - Uncommon Processes On Endpoint - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process_name, user -action.notable.param.rule_description = Prohibited software $process_name$ has been detected on $dest$ -action.notable.param.rule_title = Prohibited Software Detected On $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for applications on the endpoint that you have marked as uncommon. +action.notable.param.rule_title = Uncommon Processes On Endpoint action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -8949,49 +6993,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `uncommon_processes` |`uncommon_processes_on_endpoint_filter` [ESCU - Unload Sysmon Filter Driver - Rule] action.escu = 0 action.escu.enabled = 1 description = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Disabling Security Tools"], "mitre_technique_id": ["T1089"], "nist": ["DE.CM"]} +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search is looking to detect execution of `fltMC.exe` that specifically used for unloading the Sysmon Filter Driver +action.escu.eli5 = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. action.escu.known_false_positives = -action.escu.creation_date = 2017-10-23 +action.escu.creation_date = 2020-01-13 action.escu.modification_date = 2020-01-13 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Unload Sysmon Filter Driver - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Disabling Security Tools"] -cron_schedule = 30 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unload Sysmon Filter Driver +action.correlationsearch.label = ESCU - Unload Sysmon Filter Driver - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. -action.notable.param.rule_title = Unload Sysmon Filter Driver on $dest$ +action.notable.param.rule_title = Unload Sysmon Filter Driver action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9000,7 +7033,6 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process @@ -9008,40 +7040,31 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = This search detects loading of unsigned images by LSASS. -action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} -action.escu.eli5 = This search detects unsigned images loaded by LSASS (Local Security Authrity Subsystem Service). Normally, LSASS only loads signed images. Therefore, it is a malicious indicator when unsigned images are loaded by LSASS. This can be an indicator for credential dumping using tools like Windows Credential Editor. +action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search detects loading of unsigned images by LSASS. action.escu.how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. action.escu.known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. action.escu.creation_date = 2019-12-06 action.escu.modification_date = 2019-12-06 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Unsigned Image Loaded by LSASS - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Windows -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Credential Dumping"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unsigned Image Loaded by LSASS +action.correlationsearch.label = ESCU - Unsigned Image Loaded by LSASS - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Possible attempt at credential dumping was detected on $dest$. -action.notable.param.rule_title = unsigned images loaded by LSASS on $dest$. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search detects loading of unsigned images by LSASS. +action.notable.param.rule_title = Unsigned Image Loaded by LSASS action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, ImageLoaded -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9050,48 +7073,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = `sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_LSASS_filter` +search = `sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time) as firstTime max(_time) as lastTime by Computer, Image, ImageLoaded, Signed, SHA1 | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_LSAS`unsigned_image_loaded_by_lsass_filter` [ESCU - Unsuccessful Netbackup backups - Rule] action.escu = 0 action.escu.enabled = 1 description = This search gives you the hosts where a backup was attempted and then failed. action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -action.escu.eli5 = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +action.escu.data_models = [] +action.escu.eli5 = This search gives you the hosts where a backup was attempted and then failed. action.escu.how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. action.escu.known_false_positives = None identified -action.escu.creation_date = 2017-06-15 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 action.escu.confidence = high action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Netbackup"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Monitor Backup Solution"] -cron_schedule = 0 7 * * * -dispatch.earliest_time = -24h@h -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unsuccessful Netbackup backups +action.correlationsearch.label = ESCU - Unsuccessful Netbackup backups - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ attempted a backup but encountered an error. -action.notable.param.rule_title = Failed backup attempt by $dest$ +action.notable.param.rule_description = This search gives you the hosts where a backup was attempted and then failed. +action.notable.param.rule_title = Unsuccessful Netbackup backups action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9100,49 +7112,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature +search = `netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` [ESCU - Unusually Long Command Line - Rule] action.escu = 0 action.escu.enabled = 1 description = Command lines that are extremely long may be indicative of malicious activity on your hosts. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search calculates the average and standard deviation for the length of the command lines on each of your endpoints and alerts when it detects a command line with a length over 10 times the standard deviation larger than the average command line. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Command lines that are extremely long may be indicative of malicious activity on your hosts. action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Some legitimate applications start with long command lines. -action.escu.creation_date = 2017-08-23 +action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Unusually Long Command Line - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Unusual Processes"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unusually Long Command Line +action.correlationsearch.label = ESCU - Unusually Long Command Line - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = An unusually long command line $cmdline$ was found on $dest$ -action.notable.param.rule_title = Unusually Long Command Line on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Command lines that are extremely long may be indicative of malicious activity on your hosts. +action.notable.param.rule_title = Unusually Long Command Line action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9151,49 +7152,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process| `unusually_long_commandlines_filter`| eval threshold = 10 | where maxlen > ((threshold*stdevperhost) + avgperhost) +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 10 | where maxlen > ((threshold*stdevperhost) + avgperhost) [ESCU - Unusually Long Command Line - MLTK - Rule] action.escu = 0 action.escu.enabled = 1 description = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. -action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search leverages the Machine Learning Toolkit (MLTK) to identify outliers in the length of the command lines observed to be used by a specific user. The companion search, "Baseline of Command Line Length - MLTK," creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. action.escu.how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. action.escu.known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. action.escu.creation_date = 2019-05-08 action.escu.modification_date = 2019-05-08 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Unusually Long Command Line - MLTK - Rule action.escu.search_type = detection -action.escu.asset_at_risk = -action.escu.fields_required = ["dest", "process_name", "user"] -action.escu.entities = ["dest", "process_name", "user"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Unusual Processes"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unusually Long Command Line - MLTK +action.correlationsearch.label = ESCU - Unusually Long Command Line - MLTK - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = An unusually long command line $cmdline$ was found on $dest$ -action.notable.param.rule_title = Unusually Long Command Line on $dest$ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. +action.notable.param.rule_title = Unusually Long Command Line - MLTK action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process_name -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9202,48 +7192,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` [ESCU - Unusually Long Content-Type Length - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for unusually long strings in the Content-Type http header that the client sends the server. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} -action.escu.eli5 = This detection search uses HTTP traffic data captured with Splunk Stream. The search is constructed to use "stream:http" sourcetype and counts of the number of times an HTTP request is received by a destination which the length of the Content-Type header value the client sends the server is greater than 100 characters long. We calculate this content_type_length field and output the results. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for unusually long strings in the Content-Type http header that the client sends the server. action.escu.how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. action.escu.known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters. -action.escu.creation_date = 2017-03-14 +action.escu.creation_date = 2017-10-13 action.escu.modification_date = 2017-10-13 action.escu.confidence = high action.escu.full_search_name = ESCU - Unusually Long Content-Type Length - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Web Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unusually Long Content-Type Length +action.correlationsearch.label = ESCU - Unusually Long Content-Type Length - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip, url -action.notable.param.rule_description = This search looks for unusually long strings in the Content-Type http header +action.notable.param.rule_description = This search looks for unusually long strings in the Content-Type http header that the client sends the server. action.notable.param.rule_title = Unusually Long Content-Type Length action.notable.param.security_domain = network action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9252,48 +7231,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=stream:http | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url +search = `stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` [ESCU - WMI Permanent Event Subscription - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the creation of WMI permanent event subscriptions. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for the creation of WMI permanent event subscriptions. action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. action.escu.known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. action.escu.creation_date = 2018-10-23 action.escu.modification_date = 2018-10-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = WMI Permanent Event Subscription +action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = This search looks for the creation of a permanent WMI event subscription via Windows event logs. -action.notable.param.rule_title = WMI Event Subscription Detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for the creation of WMI permanent event subscriptions. +action.notable.param.rule_title = WMI Permanent Event Subscription action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9302,48 +7271,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest +search = `wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` [ESCU - WMI Permanent Event Subscription - Sysmon - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the creation of WMI permanent event subscriptions. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Sysmon event ID 21. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for the creation of WMI permanent event subscriptions. action.escu.how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. action.escu.known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. action.escu.creation_date = 2018-10-23 action.escu.modification_date = 2018-10-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Sysmon - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = WMI Permanent Event Subscription - Sysmon +action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Sysmon - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = This search looks for the creation of a permanent WMI event subscription via Sysmon logs. -action.notable.param.rule_title = WMI Event Subscription Detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for the creation of WMI permanent event subscriptions. +action.notable.param.rule_title = WMI Permanent Event Subscription - Sysmon action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9352,48 +7311,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter +search = `sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter` [ESCU - WMI Temporary Event Subscription - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for the creation of WMI temporary event subscriptions. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI temporary event subscription by watching for Windows event ID 5860. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for the creation of WMI temporary event subscriptions. action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. action.escu.known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. action.escu.creation_date = 2018-10-23 action.escu.modification_date = 2018-10-23 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - WMI Temporary Event Subscription - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Suspicious WMI Use"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = WMI Temporary Event Subscription +action.correlationsearch.label = ESCU - WMI Temporary Event Subscription - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for the creation of a temporary WMI event subscription via Windows event logs. -action.notable.param.rule_title = Temporary WMI Event Subscription Detected on $dest$ +action.notable.param.rule_description = This search looks for the creation of WMI temporary event subscriptions. +action.notable.param.rule_title = WMI Temporary Event Subscription action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9402,48 +7350,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = `wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` [ESCU - Web Fraud - Account Harvesting - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to identify the creation of multiple user accounts using the same email domain name. -action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["DE.CM", "DE.DP"]} -action.escu.eli5 = When a fraudster is setting the stage for a campaign, they will often create many user accounts on the website. This is a simple example of how to detect a many-account creation hosted on a Magento2 e-commerce platform, where the fraudster is using email addresses from a single email domain. +action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} +action.escu.data_models = [] +action.escu.eli5 = This search is used to identify the creation of multiple user accounts using the same email domain name. action.escu.how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. -action.escu.creation_date = 2018-07-12 +action.escu.creation_date = 2018-10-08 action.escu.modification_date = 2018-10-08 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Web Fraud - Account Harvesting - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Account -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] -action.escu.providing_technologies = ["Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = 0 1 * * * -dispatch.earliest_time = -1445m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Fraud - Account Harvesting +action.correlationsearch.label = ESCU - Web Fraud - Account Harvesting - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = src_user -action.notable.param.rule_description = This search is used to identify multiple created accounts tied to a specific email domain. Such activity is often indicative of account harvesting. A list of $src_user$ accounts were created. -action.notable.param.rule_title = Web Fraud Detection: Possible Account Harvesting +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search is used to identify the creation of multiple user accounts using the same email domain name. +action.notable.param.rule_title = Web Fraud - Account Harvesting action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9452,48 +7390,37 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=stream:http http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)"|stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain|where UniqueUsernames> 25 +search = `stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` [ESCU - Web Fraud - Anomalous User Clickspeed - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. -action.escu.mappings = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["DE.AE", "DE.CM"]} -action.escu.eli5 = It's suspicious when someone or something is moving throughout your website too quickly or with a perfect click cadence. Fortunately, it's easy to detect by calculating the time between clicks for each session and highlighting the anomalous behavior. +action.escu.mappings = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. action.escu.how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. -action.escu.creation_date = 2018-07-12 +action.escu.creation_date = 2018-10-08 action.escu.modification_date = 2018-10-08 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Web Fraud - Anomalous User Clickspeed - Rule action.escu.search_type = detection -action.escu.asset_at_risk = account -action.escu.fields_required = ["session_id"] -action.escu.entities = ["session_id"] -action.escu.providing_technologies = ["Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Fraud - Anomalous User Clickspeed +action.correlationsearch.label = ESCU - Web Fraud - Anomalous User Clickspeed - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = session_id -action.notable.param.rule_description = This search is used to examine web sessions in order to identify unnaturally rapid clicks with near-perfect cadence (high periodicity or low standard deviation), which resemble a script-driven session. -action.notable.param.rule_title = Web Fraud Detection: Anomalous User Clickspeed +action.notable.param.rule_description = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. +action.notable.param.rule_title = Web Fraud - Anomalous User Clickspeed action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = session_id -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = session_id -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9502,48 +7429,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=stream:http http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) +search = `stream_http` http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` [ESCU - Web Fraud - Password Sharing Across Accounts - Rule] action.escu = 0 action.escu.enabled = 1 description = This search is used to identify user accounts that share a common password. action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} -action.escu.eli5 = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 +action.escu.data_models = [] +action.escu.eli5 = This search is used to identify user accounts that share a common password. action.escu.how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -action.escu.creation_date = 2018-07-12 +action.escu.creation_date = 2018-10-08 action.escu.modification_date = 2018-10-08 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule action.escu.search_type = detection -action.escu.asset_at_risk = account -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] -action.escu.providing_technologies = ["Splunk Stream"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Web Fraud Detection"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Fraud - Password Sharing Across Accounts +action.correlationsearch.label = ESCU - Web Fraud - Password Sharing Across Accounts - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = This search is used to identify user accounts, $user$, that share common passwords -action.notable.param.rule_title = Web Fraud Detection: Password Sharing Across Accounts +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search is used to identify user accounts that share a common password. +action.notable.param.rule_title = Web Fraud - Password Sharing Across Accounts action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 3600s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9552,49 +7469,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 +search = `stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` [ESCU - Web Servers Executing Suspicious Processes - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for suspicious processes on all systems labeled as web servers. -action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Discovery", "System Information Discovery"], "nist": ["PR.IP"]} +action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This detection search uses the Enterprise Security Endpoint data model. The search uses tstats to search within an accelerated data model to find suspicious applications or processes such as whoami, ping, iptables, wget, service, or curl, running on hosts which are marked as web servers in the Assets and Identity Framework of ES. +action.escu.eli5 = This search looks for suspicious processes on all systems labeled as web servers. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. action.escu.known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -action.escu.creation_date = 2017-03-14 +action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 -action.escu.confidence = medium +action.escu.confidence = high action.escu.full_search_name = ESCU - Web Servers Executing Suspicious Processes - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Web Server -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Apache Struts Vulnerability"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Servers Executing Suspicious Processes +action.correlationsearch.label = ESCU - Web Servers Executing Suspicious Processes - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for suspicious processes on all systems labeled as web servers +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for suspicious processes on all systems labeled as web servers. action.notable.param.rule_title = Web Servers Executing Suspicious Processes action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 -action.risk.param.verbose = 0 +action.notable.param.severity = high alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9603,48 +7509,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` [ESCU - Windows Event Log Cleared - Rule] action.escu = 0 action.escu.enabled = 1 description = This search looks for Windows events that indicate one of the Windows event logs has been purged. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} -action.escu.eli5 = This search looks at the Windows security and system event logs. EventCode 1002 in the security log indicates that the log has been cleared, EventCode 1000 in the security log indicates the event logging service has been shut down, and EventCode 104 in the system log indicates the application log has been cleared. If any of these events are found, a notable will be generated. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} +action.escu.data_models = [] +action.escu.eli5 = This search looks for Windows events that indicate one of the Windows event logs has been purged. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. action.escu.known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. -action.escu.creation_date = 2017-02-17 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 action.escu.confidence = high action.escu.full_search_name = ESCU - Windows Event Log Cleared - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Windows Event Log Cleared +action.correlationsearch.label = ESCU - Windows Event Log Cleared - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The Event Logging System has been cleared or shutdown on $dest$ -action.notable.param.rule_title = Windows Event Log Cleared on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for Windows events that indicate one of the Windows event logs has been purged. +action.notable.param.rule_title = Windows Event Log Cleared action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, signature_id -alert.suppress.period = 28800s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9653,49 +7549,38 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = ((eventtype=wineventlog_security) AND (signature_id=1102 OR signature_id=1100)) OR ((eventtype=wineventlog_system) AND signature_id=104) | stats count min(_time) as firstTime max(_time) as lastTime by signature_id dest user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = ((`wineventlog_security` AND (signature_id=1102 OR signature_id=1100)) OR ((`wineventlog_system` AND signature_id=104) | stats count min(_time) as firstTime max(_time) as lastTime by signature_id dest user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter` [ESCU - Windows hosts file modification - Rule] action.escu = 0 action.escu.enabled = 1 description = The search looks for modifications to the hosts file on all Windows endpoints across your environment. -action.escu.mappings = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} -action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = The search looks for modifications to the hosts file on all Windows endpoints across your environment. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. action.escu.known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. -action.escu.creation_date = 2017-06-07 +action.escu.creation_date = 2018-11-02 action.escu.modification_date = 2018-11-02 action.escu.confidence = high action.escu.full_search_name = ESCU - Windows hosts file modification - Rule action.escu.search_type = detection -action.escu.asset_at_risk = Endpoint -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.providing_technologies = [] action.escu.analytic_story = ["Host Redirection"] -cron_schedule = 0 * * * * -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now action.correlationsearch.enabled = 1 -action.correlationsearch.label = Windows hosts file modification +action.correlationsearch.label = ESCU - Windows hosts file modification - Rule +schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification was noted for the hosts file on $dest$. -action.notable.param.rule_title = Modification of hosts file detected on $dest$ +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The search looks for modifications to the hosts file on all Windows endpoints across your environment. +action.notable.param.rule_title = Windows hosts file modification action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 86400s action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 disabled = true @@ -9704,37 +7589,864 @@ counttype = number of events relation = greater than quantity = 0 realtime_schedule = 0 -schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` | `windows_hosts_file_modification_filter` ### END ESCU DETECTIONS ### -### ESCU INVESTIGATIONS ### -[ESCU - AWS Investigate User Activities By ARN] +### ESCU BASELINES ### + +[ESCU - Add Prohibited Processes to Enterprise Security - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security +description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. +action.escu.creation_date = 2017-09-15 +action.escu.modification_date = 2017-09-15 +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Monitor for Unauthorized Software", "SamSam Ransomware"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. +action.escu.how_to_implement = This search should be run on each new install of ESCU. +disabled = true +is_visible = false +search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count + +[ESCU - Baseline of API Calls per User ARN - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of API Calls per User ARN +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-04-09 +action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count + +[ESCU - Baseline of Command Line Length - MLTK - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of Command Line Length - MLTK +description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. +action.escu.creation_date = 2019-05-08 +action.escu.modification_date = 2019-05-08 +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Unusual Processes"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. +action.escu.how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel + +[ESCU - Baseline of DNS Query Length - MLTK - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of DNS Query Length - MLTK +description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. +action.escu.creation_date = 2019-05-08 +action.escu.modification_date = 2019-05-08 +action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] +action.escu.data_models = ["Network_Resolution"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. +action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel + +[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK +description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. +action.escu.creation_date = 2019-11-14 +action.escu.modification_date = 2019-11-14 +action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ +In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ +More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +disabled = true +is_visible = false +search = `cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1 + +[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK +description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. +action.escu.creation_date = 2019-11-14 +action.escu.modification_date = 2019-11-14 +action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ +In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ +More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +disabled = true +is_visible = false +search = `cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1 + +[ESCU - Baseline of Network ACL Activity by ARN - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.creation_date = 2018-05-21 +action.escu.modification_date = 2018-05-21 +action.escu.analytic_story = ["AWS Network ACL Activity"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. +disabled = true +is_visible = false +search = `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count + +[ESCU - Baseline of S3 Bucket deletion activity by ARN - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of S3 Bucket deletion activity by ARN +description = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.creation_date = 2018-07-17 +action.escu.modification_date = 2018-07-17 +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count + +[ESCU - Baseline of SMB Traffic - MLTK - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of SMB Traffic - MLTK +description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. +action.escu.creation_date = 2019-05-08 +action.escu.modification_date = 2019-05-08 +action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Netsh Abuse", "Ransomware"] +action.escu.data_models = ["Network_Traffic"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. +action.escu.how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel + +[ESCU - Baseline of Security Group Activity by ARN - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of Security Group Activity by ARN +description = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.creation_date = 2018-04-17 +action.escu.modification_date = 2018-04-17 +action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. +disabled = true +is_visible = false +search = `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count + +[ESCU - Baseline of blocked outbound traffic from AWS - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from AWS +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. +action.escu.creation_date = 2018-05-07 +action.escu.modification_date = 2018-05-07 +action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. +disabled = true +is_visible = false +search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count + +[ESCU - Count of Unique IPs Connecting to Ports - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports +description = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. +action.escu.creation_date = 2017-09-13 +action.escu.modification_date = 2017-09-13 +action.escu.analytic_story = [] +action.escu.data_models = ["Network_Traffic"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count + +[ESCU - Count of assets by category - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Count of assets by category +description = This search shows you every asset category you have and the assets that belong to those categories. +action.escu.creation_date = 2017-09-13 +action.escu.modification_date = 2017-09-13 +action.escu.analytic_story = ["Asset Tracking"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search shows you every asset category you have and the assets that belong to those categories. +action.escu.how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. +disabled = true +is_visible = false +search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count + +[ESCU - Create a list of approved AWS service accounts - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Create a list of approved AWS service accounts +description = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file. +action.escu.creation_date = 2018-12-03 +action.escu.modification_date = 2018-12-03 +action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate. +disabled = true +is_visible = false +search = `cloudtrail` errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count + +[ESCU - DNSTwist Domain Names - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - DNSTwist Domain Names +description = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches. +action.escu.creation_date = 2018-10-08 +action.escu.modification_date = 2018-10-08 +action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches. +action.escu.how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. +disabled = true +is_visible = false +search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count + +[ESCU - Discover DNS records - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Discover DNS records +description = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup +action.escu.creation_date = 2019-02-14 +action.escu.modification_date = 2019-02-14 +action.escu.analytic_story = ["DNS Hijacking"] +action.escu.data_models = ["Network_Resolution"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup +action.escu.how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation +disabled = true +is_visible = false +search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv + +[ESCU - Identify Systems Creating Remote Desktop Traffic - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic +description = This search counts the numbers of times the system has generated remote desktop traffic. +action.escu.creation_date = 2017-09-15 +action.escu.modification_date = 2017-09-15 +action.escu.analytic_story = [] +action.escu.data_models = ["Network_Traffic"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search counts the numbers of times the system has generated remote desktop traffic. +action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count + +[ESCU - Identify Systems Receiving Remote Desktop Traffic - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic +description = This search counts the numbers of times the system has created remote desktop traffic +action.escu.creation_date = 2017-09-15 +action.escu.modification_date = 2017-09-15 +action.escu.analytic_story = [] +action.escu.data_models = ["Network_Traffic"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search counts the numbers of times the system has created remote desktop traffic +action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count + +[ESCU - Identify Systems Using Remote Desktop - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop +description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. +action.escu.creation_date = 2019-04-01 +action.escu.modification_date = 2019-04-01 +action.escu.analytic_story = [] +action.escu.data_models = ["Endpoint"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count + +[ESCU - Monitor Successful Backups - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Monitor Successful Backups +description = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. +action.escu.creation_date = 2017-09-12 +action.escu.modification_date = 2017-09-12 +action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. +action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. +disabled = true +is_visible = false +search = `netbackup` "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE + +[ESCU - Monitor Unsuccessful Backups - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Monitor Unsuccessful Backups +description = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. +action.escu.creation_date = 2017-09-12 +action.escu.modification_date = 2017-09-12 +action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. +action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. +disabled = true +is_visible = false +search = `netbackup` "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE + +[ESCU - Previously Seen AWS Cross Account Activity - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity +description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. +action.escu.creation_date = 2018-06-04 +action.escu.modification_date = 2018-06-04 +action.escu.analytic_story = ["AWS Cross Account Activity"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. +disabled = true +is_visible = false +search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count + +[ESCU - Previously Seen AWS Provisioning Activity Sources - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen AWS Provisioning Activity Sources +description = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. +action.escu.creation_date = 2018-03-16 +action.escu.modification_date = 2018-03-16 +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count + +[ESCU - Previously Seen AWS Regions - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen AWS Regions +description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count + +[ESCU - Previously Seen Cloud Compute Creations By User - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Creations By User +description = This search builds a table of previously seen users that have launched a cloud compute instance. +action.escu.creation_date = 2018-03-15 +action.escu.modification_date = 2018-03-15 +action.escu.analytic_story = ["Cloud Cryptomining"] +action.escu.data_models = ["Cloud_Infrastructure"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen users that have launched a cloud compute instance. +action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_creations_by_user_input_filter` by Compute.src_user | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count + +[ESCU - Previously Seen Cloud Compute Images - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Images +description = This search builds a table of previously seen images used to launch cloud compute instances +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.analytic_story = ["Cloud Cryptomining"] +action.escu.data_models = ["Cloud_Infrastructure"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen images used to launch cloud compute instances +action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` by Compute.image_id | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_images | stats count + +[ESCU - Previously Seen Cloud Compute Instance Types - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Instance Types +description = This search builds a table of previously seen cloud compute instance types +action.escu.creation_date = 2019-10-03 +action.escu.modification_date = 2019-10-03 +action.escu.analytic_story = ["Cloud Cryptomining"] +action.escu.data_models = ["Cloud_Infrastructure"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen cloud compute instance types +action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_instance_types_input_filter` by Compute.instance_type | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_instance_types | stats count + +[ESCU - Previously Seen Cloud Regions - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Cloud Regions +description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +action.escu.creation_date = 2019-10-02 +action.escu.modification_date = 2019-10-02 +action.escu.analytic_story = ["Cloud Cryptomining"] +action.escu.data_models = ["Cloud_Infrastructure"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +disabled = true +is_visible = false +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=start `previously_seen_cloud_regions_input_filter` by Compute.region | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_regions | stats count + +[ESCU - Previously Seen EC2 AMIs - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen EC2 AMIs +description = This search builds a table of previously seen AMIs used to launch EC2 instances +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.analytic_story = ["AWS Cryptomining"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen AMIs used to launch EC2 instances +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | stats count + +[ESCU - Previously Seen EC2 Instance Types - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen EC2 Instance Types +description = This search builds a table of previously seen EC2 instance types +action.escu.creation_date = 2018-03-08 +action.escu.modification_date = 2018-03-08 +action.escu.analytic_story = ["AWS Cryptomining"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen EC2 instance types +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count + +[ESCU - Previously Seen EC2 Launches By User - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen EC2 Launches By User +description = This search builds a table of previously seen ARNs that have launched a EC2 instance. +action.escu.creation_date = 2018-03-15 +action.escu.modification_date = 2018-03-15 +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen ARNs that have launched a EC2 instance. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +disabled = true +is_visible = false +search = `cloudtrail` eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count + +[ESCU - Previously Seen EC2 Modifications By User - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen EC2 Modifications By User +description = This search builds a table of previously seen ARNs that have launched a EC2 instance. +action.escu.creation_date = 2018-04-05 +action.escu.modification_date = 2018-04-05 +action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search builds a table of previously seen ARNs that have launched a EC2 instance. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. +disabled = true +is_visible = false +search = `cloudtrail` `ec2_modification_api_calls` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count + +[ESCU - Previously Seen Running Windows Services - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Running Windows Services +description = This collects the services that have been started across your entire enterprise. +action.escu.creation_date = 2020-01-13 +action.escu.modification_date = 2020-01-13 +action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This collects the services that have been started across your entire enterprise. +action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. +disabled = true +is_visible = false +search = `wineventlog_system` signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | outputlookup previously_seen_running_windows_services | stats count + +[ESCU - Previously seen API call per user roles in CloudTrail - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously seen API call per user roles in CloudTrail +description = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. +action.escu.creation_date = 2018-04-16 +action.escu.modification_date = 2018-04-16 +action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search. +disabled = true +is_visible = false +search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count + +[ESCU - Previously seen S3 bucket access by remote IP - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously seen S3 bucket access by remote IP +description = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" +action.escu.creation_date = 2018-06-28 +action.escu.modification_date = 2018-06-28 +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search. +disabled = true +is_visible = false +search = `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count + +[ESCU - Previously seen command line arguments - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously seen command line arguments +description = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. +action.escu.creation_date = 2019-03-01 +action.escu.modification_date = 2019-03-01 +action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Hidden Cobra Malware", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +action.escu.data_models = ["Endpoint"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)` + +[ESCU - Previously seen users in CloudTrail - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously seen users in CloudTrail +description = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. +action.escu.creation_date = 2018-04-30 +action.escu.modification_date = 2018-04-30 +action.escu.analytic_story = ["Suspicious AWS Login Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. +disabled = true +is_visible = false +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv | stats count + +[ESCU - Systems Ready for Spectre-Meltdown Windows Patch - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch +description = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 +action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. +action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` + +[ESCU - Update previously seen users in CloudTrail - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Update previously seen users in CloudTrail +description = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. +action.escu.creation_date = 2018-04-30 +action.escu.modification_date = 2018-04-30 +action.escu.analytic_story = ["Suspicious AWS Login Activities"] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. +disabled = true +is_visible = false +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv + +[ESCU - Windows Updates Install Failures - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Windows Updates Install Failures +description = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned. +action.escu.creation_date = 2017-09-14 +action.escu.modification_date = 2017-09-14 +action.escu.analytic_story = [] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned. +action.escu.how_to_implement = You must be ingesting your Windows Update Logs +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d + +[ESCU - Windows Updates Install Successes - Baseline] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Windows Updates Install Successes +description = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned. +action.escu.creation_date = 2017-09-14 +action.escu.modification_date = 2017-09-14 +action.escu.analytic_story = [] +action.escu.data_models = [] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned. +action.escu.how_to_implement = You must be ingesting your Windows Update Logs +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d + + + +### ESCU RESPONSE TASKS ### + +[ESCU - AWS Investigate User Activities By ARN - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. -action.escu.creation_date = 2018-01-22 +action.escu.creation_date = 2019-04-30 action.escu.modification_date = 2019-04-30 action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Cloud Cryptomining", "Command and Control", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Unusual AWS EC2 Modifications"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail userIdentity.arn={user} | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType +search = | search sourcetype=aws:cloudtrail userIdentity.arn=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType -[ESCU - AWS Investigate User Activities By AccessKeyId] +[ESCU - AWS Investigate User Activities By AccessKeyId - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9745,18 +8457,17 @@ action.escu.modification_date = 2018-06-08 action.escu.analytic_story = ["AWS Cross Account Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific credentials. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["accessKeyId"] -action.escu.entities = ["accessKeyId"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail userIdentity.accessKeyId={accessKeyId} | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage +search = | search sourcetype=aws:cloudtrail userIdentity.accessKeyId=$accessKeyId$ | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage -[ESCU - AWS Investigate User Activities By Source User] +[ESCU - AWS Investigate User Activities By Source User - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9767,40 +8478,38 @@ action.escu.modification_date = 2018-06-08 action.escu.analytic_story = ["AWS Cross Account Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific ARNs. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail userIdentity.arn={src_user} | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage +search = | search sourcetype=aws:cloudtrail userIdentity.arn=$src_user$ | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage -[ESCU - AWS Network ACL Details from ID] +[ESCU - AWS Network ACL Details from ID - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - AWS Network ACL Details from ID description = This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID -action.escu.creation_date = 2018-01-18 +action.escu.creation_date = 2017-01-22 action.escu.modification_date = 2017-01-22 action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["networkAclId"] -action.escu.entities = ["networkAclId"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:description id={networkAclId} | table id account_id vpc_id network_acl_entries{}.* +search = | search sourcetype=aws:description id=$networkAclId$ | table id account_id vpc_id network_acl_entries{}.* -[ESCU - AWS Network Interface details via resourceId] +[ESCU - AWS Network Interface details via resourceId - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9811,18 +8520,17 @@ action.escu.modification_date = 2018-05-07 action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS configuration inputs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["resourceId"] -action.escu.entities = ["resourceId"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:config resourceId={resourceId} | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp +search = | search sourcetype=aws:config resourceId=$resourceId$ | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp -[ESCU - AWS S3 Bucket details via bucketName] +[ESCU - AWS S3 Bucket details via bucketName - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9833,84 +8541,80 @@ action.escu.modification_date = 2018-06-26 action.escu.analytic_story = ["Suspicious AWS S3 Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = To implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and configure your AWS inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries AWS configuration logs and returns the information about a specific S3 bucket. The information returned includes the time the S3 bucket was created, the resource ID, the region it belongs to, the value of action performed, AWS account ID, and configuration values of the access-control lists associated with the bucket. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["bucketName"] -action.escu.entities = ["bucketName"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:config resourceId={bucketName} | table resourceCreationTime resourceId awsRegion action aws_account_id supplementaryConfiguration.AccessControlList +search = | search sourcetype=aws:config resourceId=$bucketName$ | table resourceCreationTime resourceId awsRegion action aws_account_id supplementaryConfiguration.AccessControlList -[ESCU - All backup logs for host] +[ESCU - All backup logs for host - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - All backup logs for host description = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully. -action.escu.creation_date = 2017-06-19 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 action.escu.analytic_story = ["Monitor Backup Solution"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Netbackup"] -action.escu.eli5 = none -action.escu.how_to_implement = The successfully implement this search you must first send your backup logs to Splunk. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype="netbackup_logs" dest={dest} +search = | search sourcetype="netbackup_logs" dest=$dest$ -[ESCU - Amazon EKS Kubernetes activity by src_ip] +[ESCU - Amazon EKS Kubernetes activity by src ip - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative -action.escu.full_search_name = ESCU - Amazon EKS Kubernetes activity by src_ip +action.escu.full_search_name = ESCU - Amazon EKS Kubernetes activity by src ip description = This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address -action.escu.creation_date = 2020-03-24 +action.escu.creation_date = 2020-04-13 action.escu.modification_date = 2020-04-13 action.escu.analytic_story = ["Kubernetes Scanning Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch EKS inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false -search = sourcetype="aws:cloudwatchlogs:eks" sourceIPs{}={src_ip}|rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip +search = sourcetype="aws:cloudwatchlogs:eks" sourceIPs{}=$src_ip$|rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip -[ESCU - GCP Kubernetes activity by src_ip] +[ESCU - GCP Kubernetes activity by src ip - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative -action.escu.full_search_name = ESCU - GCP Kubernetes activity by src_ip +action.escu.full_search_name = ESCU - GCP Kubernetes activity by src ip description = This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address -action.escu.creation_date = 2020-03-24 +action.escu.creation_date = 2020-04-13 action.escu.modification_date = 2020-04-13 action.escu.analytic_story = ["Kubernetes Scanning Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["GCP"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] disabled = true schedule_window = auto is_visible = false search = sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp={src_ip} | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name data.resource.type -[ESCU - Get All AWS Activity From City] +[ESCU - Get All AWS Activity From City - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9921,18 +8625,17 @@ action.escu.modification_date = 2018-03-19 action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["City"] -action.escu.entities = ["City"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City={City} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode -[ESCU - Get All AWS Activity From Country] +[ESCU - Get All AWS Activity From Country - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9943,18 +8646,17 @@ action.escu.modification_date = 2018-03-19 action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves all the activity from a specific country and will create a table containing the time, country, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["Country"] -action.escu.entities = ["Country"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Country={Country} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Country=$Country$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode -[ESCU - Get All AWS Activity From IP Address] +[ESCU - Get All AWS Activity From IP Address - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9965,18 +8667,17 @@ action.escu.modification_date = 2018-03-19 action.escu.analytic_story = ["AWS Network ACL Activity", "AWS Suspicious Provisioning Activities", "Command and Control", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves all the activity from a specific IP address and will create a table containing the time, ARN, username, the type of user, the IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search sourceIPAddress={src_ip} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion, eventName, errorCode +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search sourceIPAddress=$src_ip$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion, eventName, errorCode -[ESCU - Get All AWS Activity From Region] +[ESCU - Get All AWS Activity From Region - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -9987,63 +8688,59 @@ action.escu.modification_date = 2018-03-19 action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["Region"] -action.escu.entities = ["Region"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region={Region} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode -[ESCU - Get Authentication Logs For Endpoint] +[ESCU - Get Authentication Logs For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint description = This search returns all users that have attempted to access a particular endpoint. -action.escu.creation_date = 2017-04-10 +action.escu.creation_date = 2017-11-01 action.escu.modification_date = 2017-11-01 -action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Authentication"] -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. +action.escu.eli5 = This search returns all users that have attempted to access a particular endpoint. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats count from datamodel=Authentication where Authentication.dest={dest} by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` +search = | tstats count from datamodel=Authentication where Authentication.dest=$dest$ by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` -[ESCU - Get Backup Logs For Endpoint] +[ESCU - Get Backup Logs For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. -action.escu.creation_date = 2017-08-24 +action.escu.creation_date = 2017-09-14 action.escu.modification_date = 2017-09-14 action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Netbackup"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting your backup logs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype="netbackup_logs" COMPUTERNAME={dest} | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature +search = | search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature -[ESCU - Get Certificate logs for a domain] +[ESCU - Get Certificate logs for a domain - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10054,64 +8751,59 @@ action.escu.modification_date = 2019-04-29 action.escu.analytic_story = ["Common Phishing Frameworks"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Certificates"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["domain"] -action.escu.entities = ["domain"] disabled = true schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*{domain} by All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | `drop_dm_object_name(All_Certificates)` | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$ by All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | `drop_dm_object_name(All_Certificates)` | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -[ESCU - Get DNS Server History for a host] +[ESCU - Get DNS Server History for a host - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get DNS Server History for a host description = While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours. -action.escu.creation_date = 2017-04-10 +action.escu.creation_date = 2017-11-09 action.escu.modification_date = 2017-11-09 action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "DNS Hijacking", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Host Redirection", "Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious AWS Traffic", "Suspicious DNS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false -search = | search tag=dns src_ip={src_ip} dest_port=53 | streamstats time_window=1d count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort -count +search = | search tag=dns src_ip=$src_ip$ dest_port=53 | streamstats time_window=1d count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort -count -[ESCU - Get DNS traffic ratio] +[ESCU - Get DNS traffic ratio - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get DNS traffic ratio description = This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS. -action.escu.creation_date = 2017-04-10 +action.escu.creation_date = 2017-11-09 action.escu.modification_date = 2017-11-09 action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Network_Traffic"] -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting your network traffic +action.escu.eli5 = This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip", "dest_ip"] -action.escu.entities = ["src_ip", "dest_ip"] disabled = true schedule_window = auto is_visible = false -search = | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 All_Traffic.src={src_ip} All_Traffic.dest={dest_ip} | eval ratio = (bytes_out/bytes_in) | table ratio +search = | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 All_Traffic.src=$src_ip$ All_Traffic.dest=$dest_ip$ | eval ratio = (bytes_out/bytes_in) | table ratio -[ESCU - Get EC2 Instance Details by instanceId] +[ESCU - Get EC2 Instance Details by instanceId - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10122,18 +8814,17 @@ action.escu.modification_date = 2018-02-12 action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "Unusual AWS EC2 Modifications"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["instanceId"] -action.escu.entities = ["instanceId"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id={instanceId} | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC +search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC -[ESCU - Get EC2 Launch Details] +[ESCU - Get EC2 Launch Details - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10144,87 +8835,80 @@ action.escu.modification_date = 2018-03-12 action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns some of the launch details for a EC2 instance. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId={dest} |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName | table arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName +search = | search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId=$dest$ |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName | table arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName -[ESCU - Get Email Info] +[ESCU - Get Email Info - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Email Info description = This search returns all the information Splunk might have collected a specific email message over the last 2 hours. -action.escu.creation_date = 2017-04-21 +action.escu.creation_date = 2017-11-09 action.escu.modification_date = 2017-11-09 action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Email"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns all the information Splunk might have collected a specific email message over the last 2 hours. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["message_id"] -action.escu.entities = ["message_id"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Email.All_Email | search message_id={message_id} +search = | from datamodel Email.All_Email | search message_id=$message_id$ -[ESCU - Get Emails From Specific Sender] +[ESCU - Get Emails From Specific Sender - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Emails From Specific Sender description = This search returns all the emails from a specific sender over the last 24 and next hours. -action.escu.creation_date = 2017-04-21 +action.escu.creation_date = 2017-11-09 action.escu.modification_date = 2017-11-09 action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails", "Web Fraud Detection"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Email"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns all the emails from a specific sender over the last 24 and next hours. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Email.All_Email | search src_user={src_user} +search = | from datamodel Email.All_Email | search src_user=$src_user$ -[ESCU - Get First Occurrence and Last Occurrence of a MAC Address] +[ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. -action.escu.creation_date = 2017-06-14 +action.escu.creation_date = 2017-09-13 action.escu.modification_date = 2017-09-13 action.escu.analytic_story = ["Asset Tracking"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Network_Sessions"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +action.escu.eli5 = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_mac"] -action.escu.entities = ["src_mac"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= {src_mac} by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` -[ESCU - Get History Of Email Sources] +[ESCU - Get History Of Email Sources - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10235,107 +8919,101 @@ action.escu.modification_date = 2019-02-21 action.escu.analytic_story = [] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Email"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. +action.escu.eli5 = This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] disabled = true schedule_window = auto is_visible = false search = |tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -[ESCU - Get Logon Rights Modifications For Endpoint] +[ESCU - Get Logon Rights Modifications For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint description = This search allows you to retrieve any modifications to logon rights associated with a specific host. -action.escu.creation_date = 2017-08-16 +action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 action.escu.analytic_story = ["Account Monitoring and Controls"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search allows you to retrieve any modifications to logon rights associated with a specific host. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) dest={dest} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature +search = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature -[ESCU - Get Logon Rights Modifications For User] +[ESCU - Get Logon Rights Modifications For User - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Logon Rights Modifications For User description = This search allows you to retrieve any modifications to logon rights for a specific user account. -action.escu.creation_date = 2017-08-16 +action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 action.escu.analytic_story = ["Account Monitoring and Controls"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search allows you to retrieve any modifications to logon rights for a specific user account. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) user={user} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature +search = | search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature -[ESCU - Get Notable History] +[ESCU - Get Notable History - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Notable History description = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation. -action.escu.creation_date = 2017-03-15 +action.escu.creation_date = 2017-09-20 action.escu.modification_date = 2017-09-20 -action.escu.analytic_story = ["AWS Cross Account Activity", "AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual AWS EC2 Modifications", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Cross Account Activity", "AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual AWS EC2 Modifications", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = none -action.escu.how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | search `notable` | search dest={dest} | table _time, rule_name, owner, priority, severity, status_description +search = | search `notable` | search dest=$dest$ | table _time, rule_name, owner, priority, severity, status_description -[ESCU - Get Notable Info] +[ESCU - Get Notable Info - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Notable Info description = This search queries the notable index to retrieve detailed information captured within the notable. Every notable has a unique ID associated with it, which is used to point us directly to the notable event under investigation. -action.escu.creation_date = 2017-03-15 +action.escu.creation_date = 2017-09-20 action.escu.modification_date = 2017-09-20 -action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = none -action.escu.how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries the notable index to retrieve detailed information captured within the notable. Every notable has a unique ID associated with it, which is used to point us directly to the notable event under investigation. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["event_id"] -action.escu.entities = ["event_id"] disabled = true schedule_window = auto is_visible = false -search = | search `notable_by_id({event_id})` | table time, rule_name, dest, dest_asset_id, dest_owner, priority, severity, owner, status_description +search = | search `notable_by_id($event_id$)` | table time, rule_name, dest, dest_asset_id, dest_owner, priority, severity, owner, status_description -[ESCU - Get Outbound Emails to Hidden Cobra Threat Actors] +[ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10346,42 +9024,38 @@ action.escu.modification_date = 2018-06-14 action.escu.analytic_story = [] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Email"] -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns the information of the users that sent emails to the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, and from `redhat@gmail.com`. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_user", "recipient"] -action.escu.entities = ["src_user", "recipient"] disabled = true schedule_window = auto is_visible = false search = | from datamodel Email.All_Email | search recipient=misswang8107@gmail.com OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time) as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -[ESCU - Get Parent Process Info] +[ESCU - Get Parent Process Info - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Parent Process Info description = This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest -action.escu.creation_date = 2017-08-22 +action.escu.creation_date = 2019-02-28 action.escu.modification_date = 2019-02-28 -action.escu.analytic_story = ["Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Phishing Payloads", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Phishing Payloads", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["process_name", "dest"] -action.escu.entities = ["process_name", "dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = {process_name} Processes.dest = {dest} by Processes.user Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` +search = | tstats `summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name = $process_name$ Processes.dest = $dest$ by Processes.user Processes.parent_process_name Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` -[ESCU - Get Process File Activity] +[ESCU - Get Process File Activity - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10392,65 +9066,59 @@ action.escu.modification_date = 2019-11-06 action.escu.analytic_story = ["DHS Report TA18-074A"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. +action.escu.eli5 = This search returns the file activity for a specific process on a specific endpoint +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["process_id", "dest"] -action.escu.entities = ["process_id", "dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, values(Filesystem.process_id) as process_id from datamodel=Endpoint.Filesystem where Filesystem.dest={dest} Filesystem.process_id={process_id} by Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)` | sort _time | table _time, process_id, dest, action, file_name, file_path +search = | tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, values(Filesystem.process_id) as process_id from datamodel=Endpoint.Filesystem where Filesystem.dest=$dest$ Filesystem.process_id=$process_id$ by Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)` | sort _time | table _time, process_id, dest, action, file_name, file_path -[ESCU - Get Process Info] +[ESCU - Get Process Info - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Process Info description = This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address. -action.escu.creation_date = 2017-03-15 +action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 -action.escu.analytic_story = ["AWS Network ACL Activity", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. +action.escu.eli5 = This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["process_name", "dest"] -action.escu.entities = ["process_name", "dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Proceses.dest={dest} Proceses.process_name={process_name} by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Proceses.dest=$dest$ Proceses.process_name=$process_name$ by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` -[ESCU - Get Process Information For Port Activity] +[ESCU - Get Process Information For Port Activity - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Process Information For Port Activity description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. -action.escu.creation_date = 2017-06-25 +action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 -action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Lateral Movement", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Use of Cleartext Protocols"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "DHS Report TA18-074A", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Lateral Movement", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "SamSam Ransomware", "Suspicious AWS Traffic", "Use of Cleartext Protocols"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel +action.escu.eli5 = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest_port", "dest"] -action.escu.entities = ["dest_port", "dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = {dest} by Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port={dest_port} by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] +search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=$dest_port$ by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] -[ESCU - Get Process Registry Activity] +[ESCU - Get Process Registry Activity - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10461,111 +9129,101 @@ action.escu.modification_date = 2019-11-06 action.escu.analytic_story = ["DHS Report TA18-074A"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. +action.escu.eli5 = This search returns the registry activity for a specific process on a specific endpoint +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["process_id", "dest"] -action.escu.entities = ["process_id", "dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Registry.registry_key_name) as registry_key_name, values(Registry.dest) as dest, values(Registry.process_id) as process_id from datamodel=Endpoint.Registry where Registry.process_id={process_id} AND Registry.dest={dest} by Registry.registry_path, Registry.action, _time | `drop_dm_object_name(Registry)` | sort _time | table _time, process_id, dest, action, registry_key_name, registry_path +search = | tstats `security_content_summariesonly` values(Registry.registry_key_name) as registry_key_name, values(Registry.dest) as dest, values(Registry.process_id) as process_id from datamodel=Endpoint.Registry where Registry.process_id=$process_id$ AND Registry.dest=$dest$ by Registry.registry_path, Registry.action, _time | `drop_dm_object_name(Registry)` | sort _time | table _time, process_id, dest, action, registry_key_name, registry_path -[ESCU - Get Process Responsible For The DNS Traffic] +[ESCU - Get Process Responsible For The DNS Traffic - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Process Responsible For The DNS Traffic description = While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic. -action.escu.creation_date = 2017-04-10 +action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 action.escu.analytic_story = ["AWS Network ACL Activity", "Brand Monitoring", "Command and Control", "Data Protection", "Dynamic DNS", "Hidden Cobra Malware", "Suspicious AWS Traffic", "Suspicious DNS Traffic"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. +action.escu.eli5 = While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = {dest} by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] +search = | tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` | rename src as dest] -[ESCU - Get Registry Activities] +[ESCU - Get Registry Activities - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Registry Activities description = This search queries the Endpoint Datamodel to give you details of the latest registry values for a specific destination computer. -action.escu.creation_date = 2018-08-07 +action.escu.creation_date = 2019-03-01 action.escu.modification_date = 2019-03-01 -action.escu.analytic_story = ["DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Persistence Techniques", "Windows Privilege Escalation"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Emotet Malware DHS Report TA18-201A ", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Persistence Techniques", "Windows Privilege Escalation"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Endpoint"] -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries the Endpoint Datamodel to give you details of the latest registry values for a specific destination computer. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path values(Registry.registry_key_name) as registry_key_name count FROM datamodel=Endpoint.Registry where Registry.dest = "{dest}" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | join [| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by Processes.process_id | `drop_dm_object_name("Processes")`] +search = | tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path values(Registry.registry_key_name) as registry_key_name count FROM datamodel=Endpoint.Registry where Registry.dest = "$dest$" by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | join [| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name = reg.exe by Processes.process_id | `drop_dm_object_name("Processes")`] -[ESCU - Get Risk Modifiers For Endpoint] +[ESCU - Get Risk Modifiers For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Risk Modifiers For Endpoint description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk\_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) -action.escu.creation_date = 2017-10-14 +action.escu.creation_date = 2017-10-19 action.escu.modification_date = 2017-10-19 -action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Risk"] -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = none -action.escu.how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk\_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel:Risk.All_Risk | search risk_object_type=system risk_object={dest} | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | from datamodel:Risk.All_Risk | search risk_object_type=system risk_object=$dest$ | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` -[ESCU - Get Risk Modifiers For User] +[ESCU - Get Risk Modifiers For User - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Risk Modifiers For User description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk_scores, names of the correlation searches that contributed to create a risk score for a specific user -action.escu.creation_date = 2017-10-14 +action.escu.creation_date = 2017-10-19 action.escu.modification_date = 2017-10-19 -action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor Backup Solution", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Risk"] -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = none -action.escu.how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk_scores, names of the correlation searches that contributed to create a risk score for a specific user +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel:Risk.All_Risk | search risk_object_type=user risk_object={user} | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` +search = | from datamodel:Risk.All_Risk | search risk_object_type=user risk_object=$user$ | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` -[ESCU - Get Sysmon WMI Activity for Host] +[ESCU - Get Sysmon WMI Activity for Host - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10576,18 +9234,17 @@ action.escu.modification_date = 2018-10-23 action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Sysmon"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search queries Sysmon WMI events for the host of interest. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["process", "dest"] -action.escu.entities = ["process", "dest"] disabled = true schedule_window = auto is_visible = false -search = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host={dest} | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter +search = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter -[ESCU - Get Update Logs For Endpoint] +[ESCU - Get Update Logs For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10595,89 +9252,83 @@ action.escu.full_search_name = ESCU - Get Update Logs For Endpoint description = This search will tell you give you the update logs for a specific endpoint for the last week. action.escu.creation_date = 2017-08-24 action.escu.modification_date = 2017-08-24 -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Monitor for Unauthorized Software", "Ransomware", "SamSam Ransomware"] +action.escu.analytic_story = ["Emotet Malware DHS Report TA18-201A ", "Monitor for Unauthorized Software", "Ransomware", "SamSam Ransomware"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Updates"] -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.eli5 = none -action.escu.how_to_implement = You need to be ingesting the update logs from your various systems. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search will tell you give you the update logs for a specific endpoint for the last week. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Updates.Updates | search (vendor_product="Microsoft Windows" OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest={dest} +search = | from datamodel Updates.Updates | search (vendor_product="Microsoft Windows" OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest=$dest$ -[ESCU - Get User Information from Identity Table] +[ESCU - Get User Information from Identity Table - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get User Information from Identity Table description = Gather more information about the user identified in the Notable Event. -action.escu.creation_date = 2017-04-10 +action.escu.creation_date = 2017-09-20 action.escu.modification_date = 2017-09-20 -action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "Cloud Cryptomining", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router & Infrastructure Security", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "Cloud Cryptomining", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = Gather more information about the user identified in the Notable Event. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = | `identities` | search identity={user} | table _time, identity, first, last, email, category, watchlist +search = | `identities` | search identity=$user$ | table _time, identity, first, last, email, category, watchlist -[ESCU - Get Vulnerability Logs For Endpoint] +[ESCU - Get Vulnerability Logs For Endpoint - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Vulnerability Logs For Endpoint description = This search will show you any vulnerabilities noted for a specific endpoint for the last week. -action.escu.creation_date = 2017-08-24 +action.escu.creation_date = 2017-09-10 action.escu.modification_date = 2017-09-10 -action.escu.analytic_story = ["ColdRoot MacOS RAT", "DHS Report TA18-074A", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Ransomware", "SamSam Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["ColdRoot MacOS RAT", "DHS Report TA18-074A", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Ransomware", "SamSam Ransomware", "Windows Log Manipulation"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Vulnerabilities"] -action.escu.providing_technologies = ["Nessus"] -action.escu.eli5 = none -action.escu.how_to_implement = You need to be ingesting the logs from your vulnerability scanner. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search will show you any vulnerabilities noted for a specific endpoint for the last week. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Vulnerabilities.Vulnerabilities | search dest={dest} +search = | from datamodel Vulnerabilities.Vulnerabilities | search dest=$dest$ -[ESCU - Get Web Session Information via session_id] +[ESCU - Get Web Session Information via session id - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative -action.escu.full_search_name = ESCU - Get Web Session Information via session_id +action.escu.full_search_name = ESCU - Get Web Session Information via session id description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. action.escu.creation_date = 2018-10-08 action.escu.modification_date = 2018-10-08 action.escu.analytic_story = ["Web Fraud Detection"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Stream"] -action.escu.eli5 = none -action.escu.how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["session_id"] -action.escu.entities = ["session_id"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=stream:http {session_id} | stats values(url) values(http_user_agent) by src_ip status +search = | search sourcetype=stream:http $session_id$ | stats values(url) values(http_user_agent) by src_ip status -[ESCU - Investigate AWS ECR container listing activity] +[ESCU - Investigate AWS ECR container listing activity - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10685,22 +9336,20 @@ action.escu.full_search_name = ESCU - Investigate AWS ECR container listing acti description = This search lists all the users performing a list image operation on AWS Elastic Container Registry. Listing source user, image id, source IP, user type, http user agent. This search also gives counts of unique user agents per listing source. action.escu.creation_date = 2020-02-20 action.escu.modification_date = 2020-02-20 -action.escu.analytic_story = ["Container Implantation Monitoring & Investigation"] +action.escu.analytic_story = ["Container Implantation Monitoring and Investigation"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.You must also install Cloud Infrastructure Data Model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the users performing a list image operation on AWS Elastic Container Registry. Listing source user, image id, source IP, user type, http user agent. This search also gives counts of unique user agents per listing source. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_user"] -action.escu.entities = ["src_user"] disabled = true schedule_window = auto is_visible = false search = |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count > 1 -[ESCU - Investigate AWS User Activities by user field] +[ESCU - Investigate AWS User Activities by user field - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10711,18 +9360,17 @@ action.escu.modification_date = 2018-03-12 action.escu.analytic_story = ["AWS User Monitoring"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail user={user} | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType +search = | search sourcetype=aws:cloudtrail user=$user$ | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType -[ESCU - Investigate AWS activities via region name] +[ESCU - Investigate AWS activities via region name - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10733,41 +9381,38 @@ action.escu.modification_date = 2018-02-09 action.escu.analytic_story = ["AWS Cryptomining", "Cloud Cryptomining", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the user activities logged by CloudTrail for a specific region in question and will create a table of the values of parameters requested, the type of the event and the response from the AWS API by each user +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["awsRegion"] -action.escu.entities = ["awsRegion"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail awsRegion={awsRegion}| rename requestParameters.instancesSet.items{}.instanceId as instanceId| stats values(eventName) by userName instanceId +search = | search sourcetype=aws:cloudtrail awsRegion=$awsRegion$| rename requestParameters.instancesSet.items{}.instanceId as instanceId| stats values(eventName) by userName instanceId -[ESCU - Investigate Cloud Compute Instance Activities] +[ESCU - Investigate Cloud Compute Instance Activities - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate Cloud Compute Instance Activities description = This search returns a logs of events that operated on the compute instance. -action.escu.creation_date = 2019-10-02 +action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 action.escu.analytic_story = ["Cloud Cryptomining"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns a logs of events that operated on the compute instance. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel:Cloud_Infrastructure.Compute | search dest={dest} | fields - _* +search = | from datamodel:Cloud_Infrastructure.Compute | search dest=$dest$ | fields - _* -[ESCU - Investigate Failed Logins for Multiple Destinations] +[ESCU - Investigate Failed Logins for Multiple Destinations - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10778,42 +9423,38 @@ action.escu.modification_date = 2019-12-10 action.escu.analytic_story = ["Credential Dumping"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Authentication"] -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. +action.escu.eli5 = This search returns failed logins to multiple destinations by user. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` -[ESCU - Investigate Network Traffic From src_ip] +[ESCU - Investigate Network Traffic From src ip - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative -action.escu.full_search_name = ESCU - Investigate Network Traffic From src_ip +action.escu.full_search_name = ESCU - Investigate Network Traffic From src ip description = This search allows you to find all the network traffic from a specific IP address. action.escu.creation_date = 2018-06-15 action.escu.modification_date = 2018-06-15 action.escu.analytic_story = ["ColdRoot MacOS RAT", "Splunk Enterprise Vulnerability CVE-2018-11409"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Network_Traffic"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Palo Alto Firewall"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search allows you to find all the network traffic from a specific IP address. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Network_Traffic.All_Traffic | search src_ip={src_ip} +search = | from datamodel Network_Traffic.All_Traffic | search src_ip=$src_ip$ -[ESCU - Investigate Okta Activity by IP Address] +[ESCU - Investigate Okta Activity by IP Address - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10824,18 +9465,17 @@ action.escu.modification_date = 2020-04-02 action.escu.analytic_story = ["Suspicious Okta Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Okta"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting Okta logs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns all okta events from a specific IP address. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false search = eventtype=okta_log src_ip={src_ip} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason -[ESCU - Investigate Okta Activity by app] +[ESCU - Investigate Okta Activity by app - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10846,18 +9486,17 @@ action.escu.modification_date = 2020-04-02 action.escu.analytic_story = ["Suspicious Okta Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Okta"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting Okta logs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns all okta events associated with a specific app +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["app"] -action.escu.entities = ["app"] disabled = true schedule_window = auto is_visible = false -search = eventtype=okta_log app={app} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason +search = eventtype=okta_log app=$app$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason -[ESCU - Investigate Pass the Hash Attempts] +[ESCU - Investigate Pass the Hash Attempts - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10868,18 +9507,17 @@ action.escu.modification_date = 2019-12-10 action.escu.analytic_story = ["Credential Dumping"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search hunts for dumped NTLM hashes used for pass the hash. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false search = `wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate | stats count earliest(_time) as first_login latest(_time) as last_login by src_user dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` -[ESCU - Investigate Pass the Ticket Attempts] +[ESCU - Investigate Pass the Ticket Attempts - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10890,18 +9528,17 @@ action.escu.modification_date = 2019-12-10 action.escu.analytic_story = ["Credential Dumping"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search hunts for dumped kerberos ticket from LSASS memory. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false search = `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest | where sum_count/max_count!=2 | rename new_user AS user -[ESCU - Investigate Previous Unseen User] +[ESCU - Investigate Previous Unseen User - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10912,19 +9549,17 @@ action.escu.modification_date = 2019-12-10 action.escu.analytic_story = ["Credential Dumping"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Authentication"] -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. +action.escu.eli5 = This search returns previous unseen user, which didn't log in for 30 days. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` -[ESCU - Investigate Successful Remote Desktop Authentications] +[ESCU - Investigate Successful Remote Desktop Authentications - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -10935,64 +9570,59 @@ action.escu.modification_date = 2018-12-14 action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement", "SamSam Ransomware"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Authentication"] -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be populating the Authentication data model with security events from your Windows event logs. +action.escu.eli5 = This search returns the source, destination, and user for all successful remote-desktop authentications. A successful authentication after a brute-force attack on a destination machine is suspicious behavior. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")`| table firstTime lastTime src src_nt_domain dest user app count | sort count -[ESCU - Investigate Suspicious Strings in HTTP Header] +[ESCU - Investigate Suspicious Strings in HTTP Header - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate Suspicious Strings in HTTP Header description = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. -action.escu.creation_date = 2017-06-26 +action.escu.creation_date = 2017-10-20 action.escu.modification_date = 2017-10-20 action.escu.analytic_story = ["Apache Struts Vulnerability"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Splunk Stream"] -action.escu.eli5 = none -action.escu.how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip", "dest_ip"] -action.escu.entities = ["src_ip", "dest_ip"] disabled = true schedule_window = auto is_visible = false -search = | search sourcetype=stream:http src_ip="{src_ip}" dest_ip="{dest_ip}" | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url +search = | search sourcetype=stream:http src_ip="$src_ip$" dest_ip="$dest_ip$" | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url -[ESCU - Investigate User Activities In All Cloud Regions] +[ESCU - Investigate User Activities In All Cloud Regions - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate User Activities In All Cloud Regions description = This search lists all the logged cloud infrastructure activities by a specific cloud user -action.escu.creation_date = 2019-10-02 +action.escu.creation_date = 2019-04-30 action.escu.modification_date = 2019-04-30 action.escu.analytic_story = ["Cloud Cryptomining"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the logged cloud infrastructure activities by a specific cloud user +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["region", "src_user"] -action.escu.entities = ["region", "src_user"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel:Cloud_Infrastructure.Compute | search user={src_user} | fields - _* +search = | from datamodel:Cloud_Infrastructure.Compute | search user=$src_user$ | fields - _* -[ESCU - Investigate User Activities In Okta] +[ESCU - Investigate User Activities In Okta - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -11003,87 +9633,80 @@ action.escu.modification_date = 2020-04-02 action.escu.analytic_story = ["Suspicious Okta Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.providing_technologies = ["Okta"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting Okta logs +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search returns all okta events by a specific user +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["user"] -action.escu.entities = ["user"] disabled = true schedule_window = auto is_visible = false -search = eventtype=okta_log user={user} | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason +search = eventtype=okta_log user=$user$ | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason -[ESCU - Investigate User Activities In Single Cloud Region] +[ESCU - Investigate User Activities In Single Cloud Region - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate User Activities In Single Cloud Region description = This search lists all the logged cloud infrastructure activities by a specific cloud user in a specific cloud region -action.escu.creation_date = 2019-10-02 +action.escu.creation_date = 2019-04-30 action.escu.modification_date = 2019-04-30 action.escu.analytic_story = ["Cloud Cryptomining"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Cloud_Infrastructure"] -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search lists all the logged cloud infrastructure activities by a specific cloud user in a specific cloud region +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["region", "src_user"] -action.escu.entities = ["region", "src_user"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel:Cloud_Infrastructure.Compute | search region={region} user={src_user} | fields - _* +search = | from datamodel:Cloud_Infrastructure.Compute | search region=$region$ user=$src_user$ | fields - _* -[ESCU - Investigate Web Activity From Host] +[ESCU - Investigate Web Activity From Host - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate Web Activity From Host description = This search allows you to find all the web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. -action.escu.creation_date = 2017-04-21 +action.escu.creation_date = 2017-11-09 action.escu.modification_date = 2017-11-09 -action.escu.analytic_story = ["Brand Monitoring", "DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "SamSam Ransomware", "Suspicious Command-Line Executions", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation"] +action.escu.analytic_story = ["Brand Monitoring", "DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "SamSam Ransomware", "Suspicious Command-Line Executions", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Web"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search allows you to find all the web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["dest"] -action.escu.entities = ["dest"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Web.Web | search src={dest} +search = | from datamodel Web.Web | search src=$dest$ -[ESCU - Investigate Web Activity From src_ip] +[ESCU - Investigate Web Activity From src ip - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative -action.escu.full_search_name = ESCU - Investigate Web Activity From src_ip +action.escu.full_search_name = ESCU - Investigate Web Activity From src ip description = This search searches for all web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. action.escu.creation_date = 2018-06-15 action.escu.modification_date = 2018-06-15 action.escu.analytic_story = ["ColdRoot MacOS RAT", "Dynamic DNS", "Splunk Enterprise Vulnerability CVE-2018-11409"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 -action.escu.data_models = ["Web"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting your web traffic and populating the web data model. +action.escu.providing_technologies = [] +action.escu.data_models = [] +action.escu.eli5 = This search searches for all web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src_ip"] -action.escu.entities = ["src_ip"] disabled = true schedule_window = auto is_visible = false -search = | from datamodel Web.Web | search src={src_ip} +search = | from datamodel Web.Web | search src=$src_ip$ -[ESCU - Investigate Web POSTs From src] +[ESCU - Investigate Web POSTs From src - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative @@ -11094,872 +9717,40 @@ action.escu.modification_date = 2018-12-06 action.escu.analytic_story = ["Apache Struts Vulnerability"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = ["Web"] -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. +action.escu.eli5 = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. +action.escu.how_to_implement = none action.escu.known_false_positives = None at this time -action.escu.fields_required = ["src"] -action.escu.entities = ["src"] disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "{src}") and like(http_method, "POST") +search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "$src$") and like(http_method, "POST") - -### END ESCU INVESTIGATIONS ### - - -### ESCU BASELINES ### - -[ESCU - Add Prohibited Processes to Enterprise Security] +[ESCU - Process Chain Analysis - Response Task] action.escu = 0 action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security -description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. -action.escu.creation_date = 2017-06-27 -action.escu.modification_date = 2017-09-15 -action.escu.analytic_story = ["Emotet Malware (DHS Report TA18-201A)", "Monitor for Unauthorized Software", "SamSam Ransomware"] +action.escu.search_type = investigative +action.escu.full_search_name = ESCU - Process Chain Analysis +description = Analyze the Process Chain and identify the malicious file. By analyzing the parent process guid and searching for the process guid, the spawning process chain can be identified. +action.escu.creation_date = 2020-04-29 +action.escu.modification_date = 2020-04-29 +action.escu.analytic_story = [] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +action.escu.providing_technologies = [] action.escu.data_models = [] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. -action.escu.how_to_implement = This search should be run on each new install of ESCU. -action.escu.known_false_positives = +action.escu.eli5 = Analyze the Process Chain and identify the malicious file. By analyzing the parent process guid and searching for the process guid, the spawning process chain can be identified. +action.escu.how_to_implement = none +action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count +search = `sysmon` EventCode=1 NOT process=*Splunk* | rename process_guid AS out_process_guid process_name AS out_process_name parent_process_guid AS out_parent_process_guid parent_process_name AS out_parent_process_name | stats count by out_process_guid out_process_name out_parent_process_guid out_parent_process_name | eval join_process_guid = out_process_guid | join join_process_guid [ search `sysmon` process_guid={process_guid} EventCode=1 | rename process_name AS sub_process_name process_guid AS sub_process_guid parent_process_name AS sub_parent_process_name parent_process_guid AS sub_parent_process_guid | stats count by sub_process_name sub_process_guid sub_parent_process_name sub_parent_process_guid | eval join_process_guid = sub_parent_process_guid] | rename sub_process_guid AS process_guid sub_process_name AS process_name out_process_guid AS parent_process_guid out_process_name AS parent_process_name out_parent_process_guid AS grandparent_process_guid out_parent_process_name AS grandparent_process_name | stats count by process_guid process_name parent_process_guid parent_process_name grandparent_process_guid grandparent_process_name | head 1 | fields - count -[ESCU - Baseline of API Calls per User ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of API Calls per User ARN -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2018-04-09 -action.escu.analytic_story = ["AWS User Monitoring"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count -[ESCU - Baseline of Command Line Length - MLTK] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of Command Line Length - MLTK -description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. -action.escu.creation_date = 2019-05-08 -action.escu.modification_date = 2019-05-08 -action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Unusual Processes"] -action.escu.data_models = ["Endpoint"] -dispatch.earliest_time = -31d@d -dispatch.latest_time = -1d@d -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems. -action.escu.how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel - -[ESCU - Baseline of DNS Query Length - MLTK] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of DNS Query Length - MLTK -description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. -action.escu.creation_date = 2019-05-08 -action.escu.modification_date = 2019-05-08 -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] -action.escu.data_models = ["Network_Resolution"] -dispatch.earliest_time = -31d@d -dispatch.latest_time = -1d@d -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel in your environment. -action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel - -[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK -description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. -action.escu.creation_date = 2019-11-14 -action.escu.modification_date = 2019-11-14 -action.escu.analytic_story = ["Cloud Cryptomining", "Suspicious AWS EC2 Activities"] -dispatch.earliest_time = -91d@d -dispatch.latest_time = -1d@d -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Create a machine-learning (ML) model to establish a baseline for how many RunInstances users do in the environment. This can help you identify excessive numbers of RunInstances which may warrant further investigation to determine if there is misuse or abuse. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ -In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ -More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1 - -[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK -description = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. -action.escu.creation_date = 2019-11-14 -action.escu.modification_date = 2019-11-14 -action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] -dispatch.earliest_time = -91d@d -dispatch.latest_time = -1d@d -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Create a machine-learning (ML) model to establish a baseline for how many TerminateInstances users do in the environment. This can help you identify excessive numbers of TerminateInstances which may warrant further investigation to determine if there is misuse or abuse. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ -In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ -More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1 - -[ESCU - Baseline of Network ACL Activity by ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -action.escu.creation_date = 2018-05-21 -action.escu.modification_date = 2018-05-21 -action.escu.analytic_story = ["AWS Network ACL Activity"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail `network_acl_events` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count - -[ESCU - Baseline of S3 Bucket deletion activity by ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of S3 Bucket deletion activity by ARN -description = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -action.escu.creation_date = 2018-07-17 -action.escu.modification_date = 2018-07-17 -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count - -[ESCU - Baseline of SMB Traffic - MLTK] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of SMB Traffic - MLTK -description = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. -action.escu.creation_date = 2019-05-08 -action.escu.modification_date = 2019-05-08 -action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Emotet Malware (DHS Report TA18-201A)", "Hidden Cobra Malware", "Netsh Abuse", "Ransomware"] -action.escu.data_models = ["Network_Traffic"] -dispatch.earliest_time = -31d@d -dispatch.latest_time = -1d@d -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur. -action.escu.how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel - -[ESCU - Baseline of Security Group Activity by ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of Security Group Activity by ARN -description = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -action.escu.creation_date = 2018-04-17 -action.escu.modification_date = 2018-04-17 -action.escu.analytic_story = ["AWS User Monitoring"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail `security_group_api_calls` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count - -[ESCU - Baseline of blocked outbound traffic from AWS] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from AWS -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. -action.escu.creation_date = 2018-04-26 -action.escu.modification_date = 2018-05-07 -action.escu.analytic_story = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count - -[ESCU - Count of Unique IPs Connecting to Ports] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports -description = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. -action.escu.creation_date = 2017-06-24 -action.escu.modification_date = 2017-09-13 -action.escu.analytic_story = [] -action.escu.data_models = ["Network_Traffic"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count - -[ESCU - Count of assets by category] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Count of assets by category -description = This search shows you every asset category you have and the assets that belong to those categories. -action.escu.creation_date = 2017-06-11 -action.escu.modification_date = 2017-09-13 -action.escu.analytic_story = ["Asset Tracking"] -action.escu.data_models = ["Identity_Management"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.eli5 = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. -action.escu.how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count - -[ESCU - Create a list of approved AWS service accounts] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Create a list of approved AWS service accounts -description = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file. -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-12-03 -action.escu.analytic_story = ["AWS User Monitoring"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to `aws_service_accounts.csv`. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count - -[ESCU - DNSTwist Domain Names] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - DNSTwist Domain Names -description = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches. -action.escu.creation_date = 2017-06-01 -action.escu.modification_date = 2018-10-08 -action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Enterprise"] -action.escu.eli5 = This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\_SA\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=` -action.escu.how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count - -[ESCU - Discover DNS records] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Discover DNS records -description = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup -action.escu.creation_date = 2019-02-14 -action.escu.modification_date = 2019-02-14 -action.escu.analytic_story = ["DNS Hijacking"] -action.escu.data_models = ["Network_Resolution"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` -action.escu.how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -action.escu.known_false_positives = Please vet the lookup created by this baseline search -action.escu.fields_required = ["query", "answer"] -action.escu.entities = ["query", "answer"] -disabled = true -schedule_window = auto -is_visible = false -search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv - -[ESCU - Identify Systems Creating Remote Desktop Traffic] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic -description = This search counts the numbers of times the system has generated remote desktop traffic. -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 -action.escu.analytic_story = [] -action.escu.data_models = ["Network_Traffic"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic. -action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count - -[ESCU - Identify Systems Receiving Remote Desktop Traffic] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic -description = This search counts the numbers of times the system has created remote desktop traffic -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 -action.escu.analytic_story = [] -action.escu.data_models = ["Network_Traffic"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.eli5 = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. -action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count - -[ESCU - Identify Systems Using Remote Desktop] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop -description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. -action.escu.creation_date = 2017-04-18 -action.escu.modification_date = 2019-04-01 -action.escu.analytic_story = [] -action.escu.data_models = ["Endpoint"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process name in the Endpoint data model. -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count - -[ESCU - Monitor Successful Backups] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Monitor Successful Backups -description = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-12 -action.escu.analytic_story = ["Monitor Backup Solution"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Netbackup"] -action.escu.eli5 = This search gives you the count and the hostname of all the systems that had a successful backup each day. -action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE - -[ESCU - Monitor Unsuccessful Backups] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Monitor Unsuccessful Backups -description = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-12 -action.escu.analytic_story = ["Monitor Backup Solution"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Netbackup"] -action.escu.eli5 = This search gives you the count and hostname of all the systems that had a backup failure each day -action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype="netbackup_logs" "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE - -[ESCU - Previously Seen AWS Cross Account Activity] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity -description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. -action.escu.creation_date = 2018-06-04 -action.escu.modification_date = 2018-06-04 -action.escu.analytic_story = ["AWS Cross Account Activity"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count - -[ESCU - Previously Seen AWS Provisioning Activity Sources] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen AWS Provisioning Activity Sources -description = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. -action.escu.creation_date = 2018-03-16 -action.escu.modification_date = 2018-03-16 -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = This search includes any event name that begins with "run" or "create," and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a **GeoIP** database to determine the physical location of this IP address. This table outputs to a file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count - -[ESCU - Previously Seen AWS Regions] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen AWS Regions -description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count - -[ESCU - Previously Seen Cloud Compute Creations By User] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Creations By User -description = This search builds a table of previously seen users that have launched a cloud compute instance. -action.escu.creation_date = 2019-10-03 -action.escu.modification_date = 2018-03-15 -action.escu.analytic_story = ["Cloud Cryptomining"] -action.escu.data_models = ["Cloud_Infrastructure"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest time for each user that has created a cloud compute instance. -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_creations_by_user_input_filter` by Compute.src_user | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count - -[ESCU - Previously Seen Cloud Compute Images] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Images -description = This search builds a table of previously seen images used to launch cloud compute instances -action.escu.creation_date = 2019-10-03 -action.escu.modification_date = 2018-03-12 -action.escu.analytic_story = ["Cloud Cryptomining"] -action.escu.data_models = ["Cloud_Infrastructure"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest time for each image id that has been seen. This table is then outputted to a csv file. -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` by Compute.image_id | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_images | stats count - -[ESCU - Previously Seen Cloud Compute Instance Types] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Cloud Compute Instance Types -description = This search builds a table of previously seen cloud compute instance types -action.escu.creation_date = 2019-10-03 -action.escu.modification_date = 2019-10-03 -action.escu.analytic_story = ["Cloud Cryptomining"] -action.escu.data_models = ["Cloud_Infrastructure"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that the compute type has been seen in our dataset. We only look for those events where an instance has been created. All of these entries will be added to the `previously_seen_cloud_compute_instance_types` lookup file, which will act as a baseline for detections. -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_instance_types_input_filter` by Compute.instance_type | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_instance_types | stats count - -[ESCU - Previously Seen Cloud Regions] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Cloud Regions -description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days -action.escu.creation_date = 2019-10-02 -action.escu.modification_date = 2019-10-02 -action.escu.analytic_story = ["Cloud Cryptomining"] -action.escu.data_models = ["Cloud_Infrastructure"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS", "Azure", "GCP"] -action.escu.eli5 = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that this region has been seen in our dataset, grouped by the region. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_cloud_regions` lookup file, which will act like a baseline for detections. -action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=start `previously_seen_cloud_regions_input_filter` by Compute.region | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_regions | stats count - -[ESCU - Previously Seen EC2 AMIs] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen EC2 AMIs -description = This search builds a table of previously seen AMIs used to launch EC2 instances -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-03-12 -action.escu.analytic_story = ["AWS Cryptomining"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | stats count - -[ESCU - Previously Seen EC2 Instance Types] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen EC2 Instance Types -description = This search builds a table of previously seen EC2 instance types -action.escu.creation_date = 2018-03-08 -action.escu.modification_date = 2018-03-08 -action.escu.analytic_story = ["AWS Cryptomining"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count - -[ESCU - Previously Seen EC2 Launches By User] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen EC2 Launches By User -description = This search builds a table of previously seen ARNs that have launched a EC2 instance. -action.escu.creation_date = 2018-03-15 -action.escu.modification_date = 2018-03-15 -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count - -[ESCU - Previously Seen EC2 Modifications By User] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen EC2 Modifications By User -description = This search builds a table of previously seen ARNs that have launched a EC2 instance. -action.escu.creation_date = 2018-04-05 -action.escu.modification_date = 2018-04-05 -action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the `ec2_modification_api_calls` macro for ease of use. This table is then outputted to a file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail `ec2_modification_api_calls` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count - -[ESCU - Previously Seen Running Windows Services] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously Seen Running Windows Services -description = This collects the services that have been started across your entire enterprise. -action.escu.creation_date = 2018-07-20 -action.escu.modification_date = 2020-01-13 -action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. In this specific log event, the `param1` field represents the "service_name" and the `param2` represents the action/status of the service. This search will create a table of the first and last time as particular Windows service was seen to be in the `running` status. -action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = eventtype=wineventlog_system signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | outputlookup previously_seen_running_windows_services | stats count - -[ESCU - Previously seen API call per user roles in CloudTrail] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously seen API call per user roles in CloudTrail -description = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. -action.escu.creation_date = 2018-04-01 -action.escu.modification_date = 2018-04-16 -action.escu.analytic_story = ["AWS User Monitoring"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as "userName" in the CloudTrail logs. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count - -[ESCU - Previously seen S3 bucket access by remote IP] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously seen S3 bucket access by remote IP -description = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" -action.escu.creation_date = 2018-06-28 -action.escu.modification_date = 2018-06-28 -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the S3 bucket. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count - -[ESCU - Previously seen command line arguments] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously seen command line arguments -description = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2019-03-01 -action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Hidden Cobra Malware", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] -action.escu.data_models = ["Endpoint"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)` - -[ESCU - Previously seen users in CloudTrail] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Previously seen users in CloudTrail -description = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. -action.escu.creation_date = 2018-02-23 -action.escu.modification_date = 2018-04-30 -action.escu.analytic_story = ["Suspicious AWS Login Activities"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -action.escu.known_false_positives = n/a -action.escu.fields_required = ["user", "src"] -action.escu.entities = ["user", "src"] -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv | stats count - -[ESCU - Systems Ready for Spectre-Meltdown Windows Patch] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch -description = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 -action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] -action.escu.data_models = ["Change_Analysis"] -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.eli5 = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. -action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` - -[ESCU - Update previously seen users in CloudTrail] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Update previously seen users in CloudTrail -description = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. -action.escu.creation_date = 2019-04-25 -action.escu.modification_date = 2018-04-30 -action.escu.analytic_story = ["Suspicious AWS Login Activities"] -dispatch.earliest_time = -60m@m -dispatch.latest_time = m@m -action.escu.providing_technologies = ["AWS"] -action.escu.eli5 = In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -action.escu.known_false_positives = n/a -action.escu.fields_required = ["user", "src"] -action.escu.entities = ["user", "src"] -disabled = true -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv - -[ESCU - Windows Updates Install Failures] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Windows Updates Install Failures -description = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned. -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-14 -action.escu.analytic_story = [] -action.escu.data_models = ["Updates"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = This search gives you the count of the number of systems that attempted and failed to install a Windows update each day. -action.escu.how_to_implement = You must be ingesting your Windows Update Logs -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d - -[ESCU - Windows Updates Install Successes] -action.escu = 0 -action.escu.enabled = 1 -action.escu.search_type = support -action.escu.full_search_name = ESCU - Windows Updates Install Successes -description = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned. -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-14 -action.escu.analytic_story = [] -action.escu.data_models = ["Updates"] -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.eli5 = This search gives you the count and name of all the systems that had a successful update applied each day -action.escu.how_to_implement = You must be ingesting your Windows Update Logs -action.escu.known_false_positives = -disabled = true -schedule_window = auto -is_visible = false -search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d +### END ESCU RESPONSE TASKS ### ### USAGE DASHBOARD CONFIGURATIONS ### diff --git a/package/default/transforms.conf b/package/default/transforms.conf index e80f45becc..e3e880572b 100644 --- a/package/default/transforms.conf +++ b/package/default/transforms.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-05-25T14:45:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# diff --git a/package/default/use_case_library.conf b/package/default/use_case_library.conf index ace2c52f36..06455a4bae 100644 --- a/package/default/use_case_library.conf +++ b/package/default/use_case_library.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-05-25T14:45:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -10,11 +10,11 @@ [analytic_story://AWS Cross Account Activity] category = Cloud Security last_updated = 2018-06-04 -version = 1.0 +version = 1 references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - Previously Seen AWS Cross Account Activity"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId"] description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ @@ -23,11 +23,11 @@ This Analytic Story includes searches that will help you monitor your AWS CloudT [analytic_story://AWS Cryptomining] category = Cloud Security last_updated = 2018-03-08 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 Launches By User"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -37,22 +37,22 @@ This Analytic Story is focused on detecting suspicious new instances in your EC2 [analytic_story://AWS Network ACL Activity] category = Cloud Security last_updated = 2018-05-21 -version = 2.0 +version = 2 references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. [analytic_story://AWS Suspicious Provisioning Activities] category = Cloud Security last_updated = 2018-03-16 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Previously Seen AWS Provisioning Activity Sources"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City"] description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. narrative = Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. @@ -60,11 +60,11 @@ This Analytic Story was designed to provide you with flexibility in the precisio [analytic_story://AWS User Monitoring] category = Cloud Security last_updated = 2018-03-12 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate AWS User Activities by user field", "ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Create a list of approved AWS service accounts", "ESCU - Previously seen API call per user roles in CloudTrail"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field"] description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ @@ -74,22 +74,22 @@ The detection searches in this Analytic Story are designed to help you uncover A [analytic_story://Account Monitoring and Controls] category = Best Practices last_updated = 2017-09-06 -version = 1.0 -references = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +version = 1 +references = [] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. [analytic_story://Apache Struts Vulnerability] category = Vulnerability last_updated = 2018-12-06 -version = 1.0 +version = 1 references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] -maintainers = [{"company": "Splunk", "email": "jhernandez@splunk.com", "name": "Jose Hernandez"}] -spec_version = 2 -searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ @@ -109,22 +109,22 @@ It can also be very helpful to examine various behaviors of the process of inter [analytic_story://Asset Tracking] category = Best Practices last_updated = 2017-09-13 -version = 1.0 +version = 1 references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Count of assets by category"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. [analytic_story://Brand Monitoring] category = Abuse last_updated = 2017-12-19 -version = 1.0 +version = 1 references = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ @@ -133,11 +133,11 @@ Notable events will include IP addresses, URLs, and user data. Drilling down can [analytic_story://Cloud Cryptomining] category = Cloud Security last_updated = 2019-10-02 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Regions"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable History", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -147,11 +147,11 @@ This Analytic Story is focused on detecting suspicious new instances in your clo [analytic_story://ColdRoot MacOS RAT] category = Malware last_updated = 2019-01-09 -version = 1.0 +version = 1 references = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] -maintainers = [{"company": "Splunk", "email": "jhernandez@splunk.com", "name": "Jose Hernandez"}] -spec_version = 2 -searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +maintainers = "Jose Hernandez, Splunk" +spec_version = 3 +searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ @@ -160,11 +160,11 @@ Searches in this Analytic Story leverage the capabilities of OSquery to address [analytic_story://Collection and Staging] category = Adversary Tactics last_updated = 2020-02-03 -version = 1.1 +version = 1 references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ @@ -173,11 +173,11 @@ Use the searches to detect and monitor suspicious behavior related to these acti [analytic_story://Command and Control] category = Adversary Tactics last_updated = 2018-06-01 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. @@ -185,22 +185,22 @@ Because this communication is so critical for an adversary, they often use techn [analytic_story://Common Phishing Frameworks] category = Adversary Tactics last_updated = 2019-04-29 -version = 1.0 +version = 1 references = ["https://github.com/kgretzky/evilginx2", "https://attack.mitre.org/techniques/T1192/", "https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/"] -maintainers = [{"company": "Splunk", "email": "research@splunk.com", "name": "Splunk Research Team"}] -spec_version = 2 +maintainers = "Splunk Research Team, Splunk" +spec_version = 3 searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule", "ESCU - Get Certificate logs for a domain"] description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. narrative = As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2. -[analytic_story://Container Implantation Monitoring & Investigation] +[analytic_story://Container Implantation Monitoring and Investigation] category = Cloud Security -last_updated = -version = 1.0 +last_updated = 2020-02-20 +version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com, rvaldez@splunk.com", "name": "Rod Soto, Rico Valdez"}] -spec_version = 2 +maintainers = "Rod Soto, Rico Valdez, Splunk" +spec_version = 3 searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule", "ESCU - Investigate AWS ECR container listing activity"] description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. narrative = Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. @@ -208,11 +208,11 @@ narrative = Container Registrys provide a way for organizations to keep customiz [analytic_story://Credential Dumping] category = Adversary Tactics last_updated = 2020-02-04 -version = 3.0 +version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}, {"company": "Splunk", "email": "pbareiss@splunk.com", "name": "Patrick Bareiss"}] -spec_version = 2 -searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts"] description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ @@ -221,11 +221,11 @@ The detection searches in this Analytic Story monitor access to the Local Securi [analytic_story://DHS Report TA18-074A] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Create local admin accounts using net.exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Registry Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ @@ -235,11 +235,11 @@ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to mo [analytic_story://DNS Amplification Attacks] category = Abuse last_updated = 2016-09-13 -version = 1.0 +version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. @@ -247,11 +247,11 @@ The search in this story can help you to detect if attackers are abusing your co [analytic_story://DNS Hijacking] category = Adversary Tactics last_updated = 2020-02-04 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Discover DNS records"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Get DNS Server History for a host"] description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ @@ -266,44 +266,44 @@ The searches in this Analytic Story help you detect and investigate activities t [analytic_story://Data Protection] category = Abuse last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. [analytic_story://Disabling Security Tools] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 +version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). [analytic_story://Dynamic DNS] category = Malware last_updated = 2018-09-06 -version = 2.0 +version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src_ip"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. -[analytic_story://Emotet Malware (DHS Report TA18-201A)] +[analytic_story://Emotet Malware DHS Report TA18-201A ] category = Malware last_updated = 2020-01-27 -version = 1.0 +version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security", "ESCU - Baseline of SMB Traffic - MLTK"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ @@ -312,11 +312,11 @@ The searches in this Analytic Story will help you find executables that are rare [analytic_story://Hidden Cobra Malware] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Create or delete windows shares using net.exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ @@ -326,22 +326,22 @@ Among other searches in this Analytic Story is a detection search that looks for [analytic_story://Host Redirection] category = Abuse last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. [analytic_story://JBoss Vulnerability] category = Vulnerability last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info"] description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ @@ -362,22 +362,22 @@ It can also be helpful to examine various behaviors of and the parent of the pro [analytic_story://Kubernetes Scanning Activity] category = Cloud Security last_updated = 2020-04-15 -version = 1.0 +version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com", "name": "Rod Soto"}] -spec_version = 2 -searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src_ip", "ESCU - GCP Kubernetes activity by src_ip", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = "Rod Soto, Splunk" +spec_version = 3 +searches = ["ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. [analytic_story://Lateral Movement] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 -references = ["https://blog.binarydefense.com/reliably-detecting-pass-the-hash-through-event-log-analysis", "https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Successful Remote Desktop Authentications"] +version = 2 +references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ @@ -388,11 +388,11 @@ If there is evidence of lateral movement, it is imperative for analysts to colle [analytic_story://Malicious PowerShell] category = Adversary Tactics last_updated = 2017-08-23 -version = 4.0 +version = 4 references = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ @@ -408,22 +408,22 @@ In the event a system is suspected of having been compromised via a malicious we [analytic_story://Monitor Backup Solution] category = Best Practices last_updated = 2017-09-12 -version = 1.0 +version = 1 references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host"] description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. narrative = Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. [analytic_story://Monitor for Unauthorized Software] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. @@ -431,11 +431,11 @@ It is important to investigate any software identified as suspicious, in order t [analytic_story://Monitor for Updates] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://learn.cisecurity.org/20-controls-download"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ @@ -444,11 +444,11 @@ Microsoft releases updates for Windows systems on a monthly cadence. They should [analytic_story://Netsh Abuse] category = Abuse last_updated = 2017-01-05 -version = 1.0 +version = 1 references = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. @@ -456,11 +456,11 @@ To get started, run the detection search to identify parent processes of `netsh. [analytic_story://Orangeworm Attack Group] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ @@ -471,11 +471,11 @@ This Analytic Story is designed to help you detect and investigate suspicious ac [analytic_story://Phishing Payloads] category = Adversary Tactics last_updated = 2019-04-29 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] -maintainers = [{"company": "Splunk", "email": "research@splunk.com", "name": "Splunk Research Team"}] -spec_version = 2 -searches = ["ESCU - Detect Oulook.exe writing a .zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] +maintainers = "Splunk Research Team, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ @@ -489,11 +489,11 @@ This Analytic Story focuses on detecting signs that a malicious payload has been [analytic_story://Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns] category = Adversary Tactics last_updated = 2020-01-22 -version = 1.0 +version = 1 references = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] -maintainers = [{"company": "iDefense", "email": "iDefense.IntelOps@accenture.com", "name": "iDefense Cyber Espionage Team"}] -spec_version = 2 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "iDefense Cyber Espionage Team, iDefense" +spec_version = 3 +searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ @@ -527,33 +527,33 @@ If behavioral searches included in this story yield positive hits, iDefense reco [analytic_story://Prohibited Traffic Allowed or Protocol Mismatch] category = Best Practices last_updated = 2017-09-11 -version = 1.0 +version = 1 references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. [analytic_story://Ransomware] category = Malware last_updated = 2020-02-04 -version = 1.1 +version = 1 references = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. -[analytic_story://Router & Infrastructure Security] +[analytic_story://Router and Infrastructure Security] category = Best Practices last_updated = 2017-09-12 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. @@ -561,11 +561,11 @@ This Analytic Story helps you gain a better understanding of how your network de [analytic_story://SQL Injection] category = Adversary Tactics last_updated = 2017-09-19 -version = 1.0 +version = 1 references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. @@ -573,11 +573,11 @@ This Analytic Story contains a search designed to identify attempts by attackers [analytic_story://SamSam Ransomware] category = Malware last_updated = 2018-12-13 -version = 1.0 +version = 1 references = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ @@ -589,22 +589,22 @@ This Analytic Story includes searches designed to help detect and investigate si [analytic_story://Spectre And Meltdown Vulnerabilities] category = Vulnerability last_updated = 2018-01-08 -version = 1.0 +version = 1 references = ["https://meltdownattack.com/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Systems Ready for Spectre-Meltdown Windows Patch"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. [analytic_story://Splunk Enterprise Vulnerability] category = Vulnerability last_updated = 2017-09-19 -version = 1.0 +version = 1 references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ \ @@ -620,11 +620,11 @@ It is important to ensure that your Splunk deployment is being kept up to date a [analytic_story://Splunk Enterprise Vulnerability CVE-2018-11409] category = Vulnerability last_updated = 2018-06-14 -version = 1.0 +version = 1 references = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info"] description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ @@ -634,33 +634,33 @@ A detection search within this Analytic Story looks for vulnerabilities describe [analytic_story://Suspicious AWS EC2 Activities] category = Cloud Security last_updated = 2018-02-09 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Launches By User"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. [analytic_story://Suspicious AWS Login Activities] category = Cloud Security last_updated = 2019-05-01 -version = 1.0 +version = 1 references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "jbrewer@splunk.com", "name": "Jason Brewer"}] -spec_version = 2 -searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Previously seen users in CloudTrail", "ESCU - Update previously seen users in CloudTrail"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. [analytic_story://Suspicious AWS S3 Activities] category = Cloud Security last_updated = 2018-07-24 -version = 2.0 +version = 2 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName"] description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ @@ -669,11 +669,11 @@ Among things to look out for are S3 access from unfamiliar locations and by unfa [analytic_story://Suspicious AWS Traffic] category = Cloud Security last_updated = 2018-05-07 -version = 1.0 +version = 1 references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ @@ -683,33 +683,33 @@ The searches in this Analytic Story will monitor your AWS network traffic for ev [analytic_story://Suspicious Command-Line Executions] category = Adversary Tactics last_updated = 2020-02-03 -version = 2.1 +version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. [analytic_story://Suspicious DNS Traffic] category = Adversary Tactics last_updated = 2017-09-18 -version = 1.0 +version = 1 references = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. [analytic_story://Suspicious Emails] category = Adversary Tactics last_updated = 2020-01-27 -version = 1.0 +version = 1 references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ @@ -720,11 +720,11 @@ Once a phishing message has been detected, the next steps are to answer the foll [analytic_story://Suspicious MSHTA Activity] category = Adversary Tactics last_updated = 2020-02-03 -version = 1.1 +version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect mshta.exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ @@ -733,11 +733,11 @@ The searches in this story help you detect and investigate suspicious activity t [analytic_story://Suspicious Okta Activity] category = Adversary Tactics last_updated = 2020-04-02 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app"] description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ @@ -746,11 +746,11 @@ With people moving quickly to adopt web-based applications and ways to manage th [analytic_story://Suspicious WMI Use] category = Adversary Tactics last_updated = 2018-10-23 -version = 2.0 +version = 2 references = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ @@ -759,11 +759,11 @@ In the event that unauthorized WMI execution occurs, it will be important for an [analytic_story://Suspicious Windows Registry Activities] category = Adversary Tactics last_updated = 2018-05-31 -version = 1.0 +version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ @@ -772,11 +772,11 @@ narrative = Attackers are developing increasingly sophisticated techniques for h [analytic_story://Unusual AWS EC2 Modifications] category = Cloud Security last_updated = 2018-04-09 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History", "ESCU - Previously Seen EC2 Modifications By User"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId"] description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. @@ -784,11 +784,11 @@ narrative = A common attack technique is to infiltrate a cloud instance and make [analytic_story://Unusual Processes] category = Malware last_updated = 2020-02-04 -version = 2.1 +version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ @@ -797,22 +797,22 @@ In the event an unusual process is identified, it is imperative to better unders [analytic_story://Use of Cleartext Protocols] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://www.monkey.org/~dugsong/dsniff/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table"] description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. [analytic_story://Web Fraud Detection] category = Abuse last_updated = 2018-10-08 -version = 1.0 +version = 1 references = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] -maintainers = [{"company": "Splunk", "email": "Mayhem@splunk.com", "name": "Jim Apger"}] -spec_version = 2 -searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session_id"] +maintainers = "Jim Apger, Splunk" +spec_version = 3 +searches = ["ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id"] description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ @@ -824,22 +824,22 @@ Another search detects incidents wherein a single password is used across multip [analytic_story://Windows Defense Evasion Tactics] category = Adversary Tactics last_updated = 2018-05-31 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Defense_Evasion"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. [analytic_story://Windows File Extension and Association Abuse] category = Malware last_updated = 2018-01-26 -version = 1.0 +version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ @@ -850,11 +850,11 @@ Run the searches in this story to detect and investigate suspicious behavior tha [analytic_story://Windows Log Manipulation] category = Adversary Tactics last_updated = 2017-09-12 -version = 2.0 +version = 2 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Windows Event Log Cleared - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). @@ -862,33 +862,33 @@ The Analytic Story gives users two different ways to detect manipulation of Wind [analytic_story://Windows Persistence Techniques] category = Adversary Tactics last_updated = 2018-05-31 -version = 2.0 +version = 2 references = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}, {"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Path Interception By Creation Of program.exe - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = "Bhavin Patel, Splunk" +spec_version = 3 +searches = ["ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. [analytic_story://Windows Privilege Escalation] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 +version = 2 references = ["https://attack.mitre.org/tactics/TA0004/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Child Processes of Spoolsv.exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = "David Dorsey, Splunk" +spec_version = 3 +searches = ["ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. [analytic_story://Windows Service Abuse] category = Malware last_updated = 2017-11-02 -version = 3.0 +version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Previously Seen Running Windows Services"] +maintainers = "Rico Valdez, Splunk" +spec_version = 3 +searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. @@ -900,366 +900,357 @@ narrative = The Windows operating system uses a services architecture to allow f type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search\ -1. Retrieves the **AssumeRole** event\ -1. Verifies that the log entry contains a value for the account ID of the requesting account\ -1. Ensures that the requesting account ID does not match the account ID of the requested account\ -1. Pulls in the previously seen requesting and requested account IDs\ -1. Splits up and executes multiple search paths at the same.\ -1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ -1. Outputs the data to the cache file.\ -1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ -The search finishes by gathering the data that it will display to the user. +explanation = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = A network access control list (ACL) is a layer of security for your VPC that acts as a firewall for controlling traffic in and out of one or more subnets. Network ACLs with all open ports have a larger attack surface. This search looks for events within your CloudTrail logs to check if there were any Network ACLs created with ports ranging from 1024 to 65525. This search will create a table comprised of AWS account id, src, user and all parameters of the request made by the user and the server response. +explanation = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. +explanation = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. +explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances launched by a particular user against the saved baseline data in the model ec2_excessive_runinstances_v1. +explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. +explanation = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances terminated by a particular user against the saved baseline data in the model ec2_excessive_terminateinstances_v1. +explanation = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule] type = detection asset_type = Windows -confidence = high -explanation = dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump. +confidence = medium +explanation = Detect memory dumping of the LSASS process. how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule] type = detection asset_type = Amazon EKS Kubernetes cluster Pod confidence = medium -explanation = In this search we can detect unauthenticated web requests against an EKS cluster Pod, by looking at k8s authentication data, user agent and source IPs and API direct request. +explanation = This search provides detection information on unauthenticated requests against Kubernetes' Pods API how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] type = detection asset_type = Amazon EKS Kubernetes cluster -confidence = high -explanation = In this search we can detect unauthenticated web requests against an EKS cluster, by looking at k8s authentication data, user agent and source IPs. +confidence = medium +explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. +confidence = medium +explanation = Attempt to add a certificate to the untrusted certificate store how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for changes of the ExecutionPolicy in the registry. The ExecutionPolicy is a safety feature that controls the conditions under which PowerShell loads configuration files and runs scripts. Usually, the ExecutionPolicy is "Restricted" for Windows clients and "RemoteSigned" for Windows Servers, allowing only certain scripts to run. This search detects when an attacker sets the ExecutionPolicy to "Unrestricted" or "Bypass." +confidence = medium +explanation = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1086", "T1064"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["DE.CM"]} known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Attempt To Stop Security Service - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. +confidence = medium +explanation = This search looks for attempts to stop security-related services on the endpoint. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none -[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule] +[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. +confidence = medium +explanation = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - Batch File Write to System32 - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. +confidence = medium +explanation = The search looks for a batch file (.bat) written to the Windows system directory tree. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none -[savedsearch://ESCU - Child Processes of Spoolsv.exe - Rule] +[savedsearch://ESCU - Child Processes of Spoolsv exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for child processes of spoolsv.exe, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f. +explanation = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. -annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Privilege Escalation", "Exploitation for Privilege Escalation"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the `Network_Resolution` data model. +explanation = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} +annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each user back to the lookup file and then clears out any output. The other part of the search limits the results to when the user was seen for the first time within the previous 70 minutes. It then displays the new user, the instances created by that user, and the associated times. +explanation = This search looks for cloud compute instances created by users who have not created them before. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = none [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each image ID and user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each image back to the lookup file and clears out any output. The other part of the search limits the results to when the image was seen for the first time within the previous 70 minutes. It then displays the new image, the instances created using it, the user who created it, and the associated times. +explanation = This search looks for cloud compute instances being created with previously unseen image IDs. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = none [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each instance type and user, the search returns the first time seen, last time seen, and the system. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each instance type back to the lookup file and clears out any output. The other part of the search limits the results to when the instance type was seen for the first time within the previous 70 minutes. It then displays the new instance type, the instances created using it, the user who created them, and the times associated. +explanation = Find EC2 instances being created with previously unseen instance types. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = none [savedsearch://ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = In this search, we query cloud infrastructure compute logs to look for events that indicate that an instance was started in a particular region. Using the \"previously_seen_cloud_regions\" lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The \"eval\" and \"if\" functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with \"Instance Started in a New Region.\" However, this region will be added to the list in \"previously_seen_cloud_regions.\" +explanation = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions. -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = none [savedsearch://ESCU - Common Ransomware Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. +confidence = medium +explanation = The search looks for file modifications with extensions commonly used by Ransomware how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Common Ransomware Notes - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications in the Change Analysis data model. It checks modified file names against an included lookup file, which contains the names of note files left behind by ransomware (to inform the victim how they can pay the ransom and retrieve their files). The search returns a list of files with matching names. +confidence = medium +explanation = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Create Remote Thread into LSASS - Rule] type = detection asset_type = Windows -confidence = high -explanation = This search detects the creation of a remote thread into LSASS (Local Security Authority Subsystem Service). This technique can be used by attackers to inject code into LSASS and dump the memory in order to obtain credentials. +confidence = medium +explanation = Detect remote thread creation into LSASS consistent with credential dumping. how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none -[savedsearch://ESCU - Create local admin accounts using net.exe - Rule] +[savedsearch://ESCU - Create local admin accounts using net exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as `localgroup`, `add`, or `user` that may correspond to the creation of local admin accounts or setting user/group properties. +explanation = This search looks for the creation of local administrator accounts using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none -[savedsearch://ESCU - Create or delete windows shares using net.exe - Rule] +[savedsearch://ESCU - Create or delete windows shares using net exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation/deletion of windows drive shares. Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete (hidden) file shares by appending "$" after the name of the share. Since the creation/deletion of hidden shares is a special case of detecting share creation/deletion we have commented out the regex that adds that additional matching criteria. If only hidden share detection is desired add `| regex process="\S+[$]"` before the last pipe in the search. +explanation = This search looks for the creation or deletion of hidden shares using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement"], "mitre_technique_id": ["T1077", "T1126"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Creation of Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers will first create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using Ntdsutil, Vssadmin, or Wmic. +confidence = medium +explanation = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using wmic, which is executed by Powershell. +explanation = This search detects the use of wmic and Powershell to create a shadow copy. how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of wmic to create a shadow copy. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The file system, security, sam and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied. But it is possible by creating first a shadow copy and then copy it from the shadow copy. This search will detect this attack of credential dumping. +confidence = medium +explanation = This search detects credential dumping using copy command from a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The file system, security, sam, and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied, but it can be done by creating shadow copy and then create a symlink to the shadow copy. This search will detect this attack of credential dumping. +confidence = medium +explanation = This search detects the creation of a symlink to a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search "Baseline of DNS Query Length - MLTK" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -1267,59 +1258,59 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often use random, long domain names for their attack infrastructure. This search looks at all the queries observed over the search time frame, and identifies any domains being resolved with names that are greater that 2 times the standard deviation. +explanation = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible there can be long domain names that are legitimate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. +explanation = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. -annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - DNS record changed - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. +explanation = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ -annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Deleting Shadow Copies - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for execution of vssadmin or wmic with both the "delete" and "shadows" parameters passed on the command-line. The two arguments are searched for separately because we can't predict the number of spaces between the words on the command-line. The search will return the number of times this activity was observed, and the times of the first and last event. +explanation = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Impact"], "mitre_technique_id": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect API activity from users without MFA - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of the user. +explanation = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1327,15 +1318,15 @@ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) 1. \ 1. **Label:** AWS User Type, **Field:** userIdentity.type\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the `identity_lookup_expanded` lookup file and the service accounts. Once filtered out, we output a table with the event names and count, as well as the first and last time a specific user or service is detected. +explanation = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1343,143 +1334,143 @@ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not ye 1. \ 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and city combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and country combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and region combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = To detect pass the hash activity, we look at all events with event code 4624 that specify a logon type 3 (network logons) for remote pass the hash attacks and logon type 9 for local pass the hash attacks. The search also filters out events with an account name of 'Anonymous' to help reduce false positives. +confidence = medium +explanation = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Pass the Hash"], "mitre_technique_id": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for LSASS access using Credential Dumping tools by detecting Process access with Sysmon logs (EventCode 10), TargetImage lsass.exe and GrantedAccess 0x1410 or 0x1010. This will for example detect the use of sekurlsa::logonpasswords in Mimikatz. +explanation = This search looks for reading lsass memory consistent with credential dumping. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search gathers all the answers to each system's DNS query, then filters for queries that have subdomains extracted from the EvilGinx toolkit. It will then run a regex to extract `legit_domains` from the query and remove that from the detection if it is listed in the `legit_domains.csv` +confidence = medium +explanation = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ -annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Spearphishing Link", "Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule] type = detection asset_type = Windows -confidence = low -explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five. +confidence = medium +explanation = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times a user has caused an account lockout within a four hour window and displays those users with a count greater than or equal to five. +explanation = This search detects user accounts that have been locked out a relatively high number of times in a short period. how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization. +explanation = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary. -providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] +providing_technologies = none [savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. +explanation = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule] type = detection asset_type = Windows -confidence = high -explanation = This search looks for loaded images (dll) unique for Mimikatz using Sysmon EventCode 7 logs. +confidence = medium +explanation = This search looks for reading loaded Images unique to credential dumping with Mimikatz. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Code(signature_id) 4703 (token right adjusted), where the process requesting the token change is PowerShell.exe and the requested privilege is "SeDebugPrivilege". This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. +explanation = This search looks for PowerShell requesting privileges consistent with credential dumping. how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect New Local Admin account - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is "Administrators", and determines whether they are generated for the same user's Security ID within three hours of each other. It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned. +explanation = This search looks for newly created accounts that have been elevated to local administrators. how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.\ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Security ID, **Field:** Security_ID\ 1. \ @@ -1487,116 +1478,105 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not 1. \ 1. **Label:** Message, **Field:** Message\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Valid Accounts", "Defense Evasion", "Persistence"], "nist": ["PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1078"], "nist": ["PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect New Login Attempts to Routers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a "router" in the ES assets and identity framework. +explanation = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate router connections may appear as new connections -providing_technologies = ["Active Directory", "Palo Alto Firewall"] +providing_technologies = none [savedsearch://ESCU - Detect New Open S3 buckets - Rule] type = detection asset_type = S3 Bucket confidence = medium -explanation = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. +explanation = This search looks for CloudTrail events where a user has created an open/public S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -providing_technologies = ["AWS"] +providing_technologies = none -[savedsearch://ESCU - Detect Oulook.exe writing a .zip file - Rule] +[savedsearch://ESCU - Detect Oulook exe writing a zip file - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = In this search, we are essentially trying to detect if outlook.exe is writing a `.zip` file to the disk. The way this search would run is, it will execute the the subsearch first which looks for all .zip files being written to the disk and outputs a crucial field "process_id", that we use the main search to check if that process\_id belongs to a process_name of outlook.exe. The search uses a join command to essentially give you an end result of the first and last time that zip file was written by outlook.exe, the dest and user logged on the system, the hash value and the complete path to the zip file on disk +confidence = medium +explanation = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect Outbound SMB Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private. +explanation = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Commonly Used Port", "Credential Access", "Lateral Movement"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none -[savedsearch://ESCU - Detect Path Interception By Creation Of program.exe - Rule] +[savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule] type = detection asset_type = confidence = medium -explanation = This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file "program.exe" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes. +explanation = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Tanium", "Ziften"] +providing_technologies = none -[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. +explanation = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the PsExec process with `accepteula` on the command line. +explanation = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - Detect Rare Executables - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes. +explanation = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. -annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} +annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect S3 access from a new IP - Rule] type = detection asset_type = S3 Bucket -confidence = low -explanation = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource +confidence = medium +explanation = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through a series of steps, as per the following: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1604,137 +1584,95 @@ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) 1. \ 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).\ -1. Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Count the number of API calls per Amazon Resource Name (ARN).\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is not necessary, and merge the rest of the data with the results of the stats command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. They do the same for the standard deviation--weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and set the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and return the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. -annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Exfiltration"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] type = detection asset_type = S3 Bucket confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ -1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ -1. Count the number of API calls per ARN.\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. +explanation = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls specifically for security groups.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ -1. Counts the number of blocked outbound connections by each source IP\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `numberOfBlockedConnections` as `latestCount`.\ -1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect USB device insertion - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device +confidence = medium +explanation = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Exfiltration"], "nist": ["PR.PT", "PR.DS"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection asset_type = Infrastructure confidence = medium -explanation = This search requires you to leverage the Enterprise Security Assets and Identity framework to populate assets_by_str.csv. Once the assets_by_str.csv is populated, we then query your DHCP logs to detect unknown systems connecting to your network. More documentation is available at: http://docs.splunk.com/Documentation/ES/4.7.1/Admin/Verifyassetandidentitydata. +explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. -annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none -[savedsearch://ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule] +[savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often leverage various scripting languages to execute their attacks. In a Windows environment, the Windows Script Host is the tool that interprets the scripts and is included in all modern versions of Windows. The Windows Script Host is available as a command-line tool called "cscript.exe" or "wscript.exe." To detect this behavior, the search looks for process-creation events for cscript.exe or wscript.exe with a parent process of cmd.exe. The search will return the count, the first and last times this behavior was seen on a destination machine, and user and process information. +explanation = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications may exhibit this behavior. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] type = detection asset_type = Web Server confidence = medium -explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +explanation = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery", "System Information Discovery"]} +annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +providing_technologies = none [savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query. It checks the results against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` by each host (DNS.src). +explanation = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -1742,1204 +1680,1204 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 1. \ 1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] type = detection asset_type = Web Server -confidence = high -explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +confidence = medium +explanation = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model -annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = No known false positives for this detection. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +providing_technologies = none -[savedsearch://ESCU - Detect mshta.exe running scripts in command-line arguments - Rule] +[savedsearch://ESCU - Detect mshta exe running scripts in command-line arguments - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Mshta.exe is a built-in Windows utility that can launch HTML files with .hta extensions (HTML applications), javascript, or VBScript. The search detects this behavior by looking for events where the process mshta.exe is executed with command-line arguments that indicate that a script is invoked +explanation = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect new API calls from user roles - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. +explanation = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = Attackers have a range of built-in Windows tools they leverage to ascertain the topography of a network from the point of view of a compromised machine. It is uncommon to see these commands execute quickly within short periods of time. This search returns the number of times, as well as the first time and last times, that every process has run for each endpoint. It then executes the macro `system_network_configuration_discovery_tools`, which looks for processes that are typically used for network configuration discovery. Once you have a list of suspicious process launches for each destination, you can leverage the transaction command to see what processes are fired within a five-minute span on an endpoint and detect only those events where the count of these processes is greater than five. +confidence = medium +explanation = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred. +confidence = medium +explanation = This search looks for web connections to dynamic DNS providers. how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Web Service", "Exfiltration Over Command and Control Channel", "Defense Evasion"], "nist": ["PR.IP", "DE.DP"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"]} known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +providing_technologies = none [savedsearch://ESCU - Detection of DNS Tunnels - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = The search will calculate the distinct count and sum of the length of DNS queries made and DNS answers received by a particular host to alert the analyst if the combined length is greater than 10000, which is not typical behavior. +confidence = medium +explanation = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.PT", "PR.DS"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Detection of tools built by NirSoft - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for process-creation events accompanied by specific command-line arguments ("scomma" and "stext"). These parameters may be leveraged by a set of free, legitimate tools built by NirSoft. Attackers have been seen abusing the tools' capabilities to steal passwords, set up key loggers, recover account information from mail clients, and conduct other nefarious activities. The search will identify the count, the first and last times a process is executed, the command-line arguments, and the parent process. +explanation = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Discovery", "Execution", "Lateral Movement", "Third-party Software", "Account Discovery"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072", "T1087"], "nist": ["PR.IP"]} known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Disabling Remote User Account Control - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search checks to see if the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy was modified. This registry key can be used to disable remote User Account Control. The search returns the count, the first time activity was seen, last time activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +explanation = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = LSASS is the Local Security Authority Subsystem Service, which is responsible for storing the user credentials. There are multiple ways to attack LSASS. This search detects the usage of comsvcs.dll for dumping the LSASS process. +confidence = medium +explanation = Detect the usage of comsvcs.dll for dumping the lsass process. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2_modification_api_calls`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems. +explanation = This search looks for EC2 instances being modified by users who have not previously modified them. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` +explanation = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the AMI image ID of all successful EC2 instance launches within the last hour and then appends the historical data from the lookup file to those results. It then recalculates the earliest and latest seen time field for each AMI image ID and returns only those AMI image IDs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created with previously unseen AMIs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created with previously unseen instance types. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created by users who have not created them before. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name. +confidence = medium +explanation = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Microsoft Exchange"] +providing_technologies = none [savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. +explanation = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114"]} known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Excessive DNS Failures - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks at DNS traffic with a reply code that is NOT indicative of a successful response. Numerous unsuccessful replies may be indicative of DNS protocol tampering or other malicious activity. If more than 50 of these unsuccessful responses are observed over the time frame of the search, a notable event will be generated. +explanation = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Exfiltration Over Alternative Protocol", "Command and Control", "Commonly Used Port"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension. +explanation = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Execution of File with Multiple Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. +confidence = medium +explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search finds all the successful backup messages in your logs, and then looks for the most recent backup time for each system. It then identifies those systems where the most recent successful backup time is over a week ago, and reports on them. +confidence = medium +explanation = This search returns a list of hosts that have not successfully completed a backup in over a week. how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = ["Netbackup"] +providing_technologies = none [savedsearch://ESCU - File with Samsam Extension - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and creates notable events when it identifies files with extensions associated with the SamSam ransomware, including `.stubbin`, `.berkshire`, `.satoshi`, `.sophos`, or `.keyxml`. Files with these extensions have been observed in SamSam attacks consisting of payload data or keying material. +confidence = medium +explanation = The search looks for file writes with extensions consistent with a SamSam ransomware attack. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - First Time Seen Running Windows Service - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name. +explanation = This search looks for the first time a Windows service is seen running in your environment. how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "New Service"], "mitre_technique_id": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} +annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - First time seen command line argument - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. +explanation = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - GCP GCR container uploaded - Rule] type = detection asset_type = GCP GCR Container confidence = medium -explanation = In this search we can detect if a new container has been uploaded to Google Container Registry, operator can monitor users uploading containers, object paths of new uploaded containers. +explanation = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. -annotations = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. -providing_technologies = ["GCP"] +providing_technologies = none [savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule] type = detection asset_type = GCP Kubernetes cluster -confidence = high -explanation = In this search we can detect unauthenticated web requests and possible attack against a GCP cluster, by looking at k8s authentication data, user agent, source IPs and destionation +confidence = medium +explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -providing_technologies = ["GCP"] +providing_technologies = none -[savedsearch://ESCU - Hiding Files And Directories With Attrib.exe - Rule] +[savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule] type = detection asset_type = confidence = medium -explanation = This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set. The +h flag is used to hide a file. +explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Identify New User Accounts - Rule] type = detection asset_type = Domain Server confidence = medium -explanation = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. +explanation = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -providing_technologies = ["Active Directory"] +providing_technologies = none [savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] type = detection asset_type = DNS Servers -confidence = high -explanation = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. +confidence = medium +explanation = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - MacOS - Re-opened Applications - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks at the Endpoint data model to identify any MacOS process events referencing a property list file which determines which applications are "re-opened" during startup. This could indicate a malicious attempt to establish persistence on the system. +explanation = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Persistence"], "mitre_technique_id": ["T1164"], "nist": ["DE.DP", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. -providing_technologies = ["OSquery"] +providing_technologies = none [savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes running with specific command-line arguments that indicate that the process will download a file from the Internet without display anything to the user. The search for "*-Exec*" is to check and see if the default execution policy for PowerShell is being overridden on the command-line. The search for "*-WindowStyle*" and "*hidden*" are to see if the window that would normally be displayed will be hidden from the user instead. Finally, the search for "*New-Object*" and "*System.Net.WebClient*" are there to check to see if a PowerShell object that can be used to download files will be created. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = System administrators may use this option, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to "Restricted," which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for `* -ex*`, which should catch all variations of this parameter, followed by the keyword `bypass`. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands. +explanation = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = These characters might be legitimately on the command-line, but it is not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search gathers all the answers to each system's DNS query, then filters out all queries that do not appear on the list of faux "look-a-like" domains that have been generated from the brand abuse domains you are monitoring. +confidence = medium +explanation = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at the sender address in email headers, and identifies those with a sender address using a domain name that matches the list of permutations generated for the domain you want to monitor. +confidence = medium +explanation = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Microsoft Exchange", "Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the `registry_path` field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry. +explanation = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Local Port Monitor"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} known_false_positives = You will encounter noise from legitimate print-monitor registry entries. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. +confidence = medium +explanation = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +providing_technologies = none [savedsearch://ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects instances when there are more than 5 distinct users failing Okta logins due to invalid credentails from the same IP address. This may be indicative of attack techniques such as credential stuffing or password spraying, where an attacker attempts to login using common or found passwords and attempts to authenticate with them. +confidence = medium +explanation = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. -providing_technologies = ["Okta"] +providing_technologies = none [savedsearch://ESCU - New container uploaded to AWS ECR - Rule] type = detection asset_type = AWS ECR container confidence = medium -explanation = In this search we can detect if a new container has been uploaded to Amazon Elastic Container Registry, operator can monitor users uploading containers, image ids of new uploaded containers. +explanation = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. -annotations = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. -providing_technologies = ["AWS"] +providing_technologies = none [savedsearch://ESCU - No Windows Updates in a time frame - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Keeping your systems up-to-date with the latest patches is an important step in keeping your systems secured. For Windows endpoints, Microsoft typically releases patches on the second Tuesday of every month. These patches contain fixes for vulnerabilities in the system that could potentially be exploited by malicious actors. This search checks for messages regarding Windows updates in the 'Update' data model. If a message indicating a successful update has not been observed in 60 days, a notable event will be generated. These systems should be checked to determine why it has not been updated in that time frame. +explanation = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} known_false_positives = None identified -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Okta Account Lockout Events - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects when a user exceeds the maximum configured Okta login attempts and the account is subsequently locked out. This is often indicative of brtue force attempts against a user account. +confidence = medium +explanation = Detect Okta user lockout events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. -providing_technologies = ["Okta"] +providing_technologies = none [savedsearch://ESCU - Okta Failed SSO Attempts - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search looks for events that indicate a user attempted to access an app they did not have permissions to access. This could indicate attempts to access prohibited applications. Please leverage the `okta_failed_sso_attempt_filter` macro to filter out false positives +confidence = medium +explanation = Detect failed Okta SSO events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to. -providing_technologies = ["Okta"] +providing_technologies = none [savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects users logging in from multiple states in the last 24 hours. This can be indicative of an attacker using compromised credentials to log in to Okta. The efficacy of this search is highly dependant on the mobility of the users using Okta. It is particularly useful in situations where users should explicitly *not* be travelling, such as during the COVID-19 pandemic. +confidence = medium +explanation = This search detects logins from the same user from different states in a 24 hour period. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. -providing_technologies = ["Okta"] +providing_technologies = none [savedsearch://ESCU - Open Redirect in Splunk Web - Rule] type = detection asset_type = Splunk Server confidence = medium -explanation = This search looks within Splunk's internal logs for evidence of CVE-2016-4859 open redirect exploitation attempts. +explanation = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. how_to_implement = No extra steps needed to implement this search. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = None identified -providing_technologies = ["Splunk Enterprise"] +providing_technologies = none [savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks at the Alerts data model to identify those generated from the osquery osx-attacks.conf pack, which search for the ColdRoot RAT. +explanation = This search looks for ColdRoot events from the osx-attacks osquery pack. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model -annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Execution", "Persistence", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} known_false_positives = There are no known false positives. -providing_technologies = ["OSquery"] +providing_technologies = none [savedsearch://ESCU - Overwriting Accessibility Binaries - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns all the different accessibility binaries that have been modified for each Windows host. +confidence = medium +explanation = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Accessibility Features"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Process Execution via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for processes launched via WMI, either remotely or locally, by looking for processes launched by WmiPrvSE.exe, which is the process WMI uses to execute new processes and commands. +explanation = This search looks for processes launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Processes Tapping Keyboard Events - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages Alerts generated from the osquery osx-attacks.conf pack search `Keyboard_Event_Taps` to detect when a process is monitoring the keystrokes of a machine, This is a common technique used by macOS remote access trojans to log keystrokes from a machine +explanation = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. -annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Collection"], "nist": ["DE.DP"]} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]} known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. -providing_technologies = ["OSquery"] +providing_technologies = none [savedsearch://ESCU - Processes created by netsh - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for all processes with the parent process "c:\Windows\System32\netsh.exe" and returns the process, the command line used to execute it, the host name, and the user context under which it ran. +explanation = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Processes launching netsh - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for all the parent processes of netsh.exe and returns that process, the command-line used to execute it, the host name, and the user context under which it ran. +explanation = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. +explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["DE.AE", "PR.AC"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Prohibited Software On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as "prohibited" in the Enterprise Security "Interesting Processes" table. +confidence = medium +explanation = This search looks for applications on the endpoint that you have marked as prohibited. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Protocol or Port Mismatch - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for instances in which the protocol observed is not consistent with the port and transport protocol typically used for that protocol. For example, looking for network traffic other than HTTP running over TCP port 80. Such behavior could indicate a misconfiguration or a custom command and control protocol that has been designed to look like ordinary web traffic. The search will also identify if HTTP traffic is observed running on unexpected ports. This can be common in many environments. +explanation = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port"], "nist": ["DE.AE", "PR.AC"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Protocols passing authentication in cleartext - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext. +explanation = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. -annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "mitre_attack": ["Credential Access", "Lateral Movement", "Collection"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} +annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none -[savedsearch://ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule] +[savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for modifications to registry paths that specify the definition and configuration of Windows services by reg.exe. Reg.exe is a Windows utility that allows for manipulation of the registry via the command line. Malware often uses the Windows services architecture to persist, hide in plain sight, and gain the ability to interact with the Windows kernel. While it is common to modify the configuration of Windows services (and new services may be created with software installs), the use of reg.exe to create or modify a service configuration is unusual and a technique commonly used by attackers. The search returns the count, the first time the activity was seen, the last time activity was seen, the registry path that was modified, the host where the modification took place, and the user that performed the modification. +confidence = medium +explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none -[savedsearch://ESCU - Reg.exe used to hide files/directories via registry keys - Rule] +[savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule] type = detection asset_type = confidence = medium -explanation = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory. +explanation = The search looks for command-line arguments used to hide a file or directory using the reg add command. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = None at the moment -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Registry Keys Used For Persistence - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for specific registry paths that malware often uses to ensure survivability and persistence on system startup. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +explanation = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Registry Run Keys / Start Folder", "AppInit DLLs", "Authentication Package"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for specific registry paths that malware often uses to elevate privileges. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place, and the user who performed the modification. +explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence", "Accessibility Features"], "mitre_technique_id": ["T1183"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. +explanation = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. +explanation = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Remote Desktop Network Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. +explanation = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Remote Desktop Process Running On System - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the "common_rdp_source" category applied. Categories are applied to systems using the Assets and Identity framework. +explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. -annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing native Windows utilities such as wmic.exe as a means to "live off the land", and avoid introducing new executables to the target system. In this search, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +explanation = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Remote Registry Key modifications - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for modifications made to the Windows registry from remote locations using reg.exe—a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification. +explanation = This search monitors for remote modifications to registry keys. how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence", "Lateral Movement"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Remote WMI Command Attempt - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +explanation = This search looks for wmic.exe being launched with parameters to operate on remote systems. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Administrators may use this legitimately to gather info from remote systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for rundll32.exe being run, loading a DLL out of a directory or subdirectory of AppData, and specifying the function at ordinal 2 be run. +explanation = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Execution", "Rundll32"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1085"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - SMB Traffic Spike - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. +explanation = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - SQL Injection with Long URLs - Rule] type = detection asset_type = Database Server confidence = medium -explanation = This search looks only at your web servers and returns the source, the web server, the URL and its length, and the user agent associated with HTTP GET requests for extremely long URLs or user agent lengths with more than three common SQL commands found within the URL. +explanation = This search looks for long URLs that have several SQL commands visible within them. how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. -annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Commonly Used Port"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = none [savedsearch://ESCU - Samsam Test File Write - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and monitors for a file named "test.txt" written to "windows\system32". This file is copied to potential targets during SamSam ransomware attacks to test the attacker's ability to access remote systems. If the file is successfully copied to the system, the system is added to a list of targets on which to deploy ransomware. +confidence = medium +explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none -[savedsearch://ESCU - Sc.exe Manipulating Windows Services - Rule] +[savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of sc.exe with parameters that indicate the utility is being used to create a new Windows service, or modify an existing one. Attackers often create a new service to host their malicious code, or they may take a non-critical service or one that is disabled, and modify it to point to their malware and enable the service if necessary. It is unusual for a service to be created or modified using the sc.exe utility. +explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate that a specific task "reset," whose name is associated with the Dragonfly threat actor--has been created or deleted. Schtasks.exe is a native Windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate that specific task names related to the Bad Rabbit ransomware were created or deleted. The specific task name used are rhaegal, drogon and viserion_. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Execution", "Scheduled Task"], "mitre_technique_id": ["T1053"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Script Execution via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for scripts launched via WMI, either remotely or locally, by looking for the execution of scrcons.exe, which is the scripting host used by WMI, similar to wscript or cscript. +explanation = This search looks for scripts launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Shim Database File Creation - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for files being created in `Windows\AppPatch\Custom and Windows\AppPatch\Custom64`, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint. +confidence = medium +explanation = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. +explanation = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Short Lived Windows Accounts - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Logs 4720 (account creation) and 4726 (account deletion) and determines if they happen for the same user within 4 hours of each other. It will report the user and machine that reported the events and the time it first and last saw this activity. +explanation = This search detects accounts that were created and deleted in a short time period. how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns all the processes for each endpoint and user and filters out any process that isn't 5 characters long and ends with .exe. +confidence = medium +explanation = This search looks for process names that consist only of a single letter. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the three CVEs associated with the Spectre and Meltdown vulnerabilities. +confidence = medium +explanation = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied. -providing_technologies = ["Nessus", "Qualys"] +providing_technologies = none [savedsearch://ESCU - Spike in File Writes - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = This search calculates counts the number of file modification events per hour per host in your environment. It then takes the average and standard deviations of those numbers and displays any hosts with more than 20 events that have over four times the standard deviation more than the average number of file modifications. +confidence = medium +explanation = The search looks for a sharp increase in the number of files written to a particular host how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response products, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule] type = detection asset_type = Splunk Server confidence = medium -explanation = This search searches Splunk's internal logs for evidence of CVE-2018-11409 exploitation attempts. +explanation = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. -providing_technologies = ["Splunk Enterprise"] +providing_technologies = none [savedsearch://ESCU - Suspicious Changes to File Associations - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for changes made to the registry that control Windows file associations. It is typical for users to change the file association to open certain types of files with specific applications. However, when these changes are legitimately performed, they are typically done via the processes explorer.exe or openwith.exe. The search first executes the subsearch that looks at the Registry node, which specifies setting a value in the registry and creates a table of process_id and dest. It then uses those arguments to find out what process and parent process were responsible for making those registry changes. +explanation = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This detection monitors for emails that are suspicious because of their sender, domain rareness, or behavior differences, as determined by Splunk UBA. In this search, we query the "UEBA" data model to look for anomalies that are raised by the "SuspiciousEmailDetectionModel" and will output the count, description of the anomaly, signature, the type of event in UBA, the severity, and the user who received a potentially suspicious email from a newly seen domain. It will also output all the categories associated with that anomaly. +explanation = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and whitelist the URL if you determine that it is a legitimate sender. -providing_technologies = ["Microsoft Exchange"] +providing_technologies = none [savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at any email messages with attachments and checks the file names of those attachments against an included lookup file to see if it has a suspicious file extension. +confidence = medium +explanation = This search looks for emails that have attachments with suspicious file extensions. how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Execution", "Defense Evasion"], "mitre_technique_id": ["T1193"], "nist": ["DE.AE", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified -providing_technologies = ["Microsoft Exchange"] +providing_technologies = none [savedsearch://ESCU - Suspicious File Write - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at files being created or modified in the Endpoint file-system data model. The names of those files are checked against an included lookup file, which contains the names of files associated with malware or attack activity. The search returns any files with matching names, along with a note (also specified in the lookup file) that gives or points to more information about the files. +confidence = medium +explanation = The search looks for files created with names that have been linked to malicious activity. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none [savedsearch://ESCU - Suspicious Java Classes - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages HTTP form data from typically POST events that can be captured with Splunk streams or similar wire data capture tools. The search looks for java classes like `processbuilder` and `runtime` are used to create a new process and execute commands inside java, and are synonymous with spawning a shell. There are very exceptional reasons to ever these classes in Java via an HTTP API and hence when seen are highly suspicious. Also, this is a common vectors leverage to exploit Apache Struts. +explanation = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. -annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} known_false_positives = There are no known false positives. -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +providing_technologies = none [savedsearch://ESCU - Suspicious LNK file launching a process - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = In this search, we are essentially trying to detect if a LNK file created under the C:\User* or *\Local\Temp\* directory structures is launching a process with in 1 hour of its creation. LNK files or also known as Windows shortcut files are commonly associated with phishing and are a [preferred method used for exploitation](https://www.fireeye.com/blog/threat-research/2017/04/fin7-phishing-lnk.html). +confidence = medium +explanation = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to execute process from temporary or user directories. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none -[savedsearch://ESCU - Suspicious Reg.exe Process - Rule] +[savedsearch://ESCU - Suspicious Reg exe Process - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of reg.exe with a parent process of cmd.exe. It then executes a subsearch looking for those cmd.exe processes with a parent that is not explorer.exe. It then joins those two searches to make sure that the reg.exe process is a grandchild of the non explorer.exe process. The search will return the number of such instances and the first and last time this activity has been seen on each endpoint and user. +explanation = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry", "Disabling Security Tools"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112", "T1089"], "nist": ["DE.CM"]} known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Suspicious wevtutil Usage - Rule] type = detection asset_type = confidence = medium -explanation = This search looks for execution of wevtutil.exe with command-line arguments that indicate that it has been used to delete the setup, application, security, or system event logs. The search returns the number of times the behavior was observed, the first and last time it was seen, the host exhibiting the behavior and the user context of the process execution. +explanation = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Suspicious writes to System Volume Information - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search uses data on file writes captured via Sysmon to watch for writes to the "System Volume Information" folder by processes other than the system process. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. It then looks for a file created with a path that includes "System Volume Information" and a process ID (PID) other than 4. PID 4 is assigned to the System process on Windows systems. Excluding these writes allows us to filter out legitimate activity. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +explanation = This search detects writes to the 'System Volume Information' folder by something other than the System process. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +explanation = This search detects writes to the recycle bin by a process other than explorer.exe. how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -annotations = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -providing_technologies = ["Sysmon"] +providing_technologies = none [savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search returns all the processes that are not executing out of the C:\Windows\System32 or C:\Windows\SysWOW64 directories. Next, it takes the filename and looks it up in a table `is_windows_system_file` of files that should normally run out of the C:\Windows\System32 or C:\Windows\SysWOW64 directory. Any matches are then returned. +explanation = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Masquerading"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - TOR Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages the Enterprise Security Network_Traffic data model to look for network traffic that has been identified as TOR and marked as 'allowed'. +explanation = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE"]} known_false_positives = None at this time -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - USN Journal Deletion - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of fsutil.exe with command-line arguments to delete the USN journal. The search returns the count of the number of times it's seen this process execution with these arguments, the first and last time it's seen this behavior, the hosts it was executed on, and the user context under which it was executed. +explanation = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} +annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Uncommon Processes On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns the number of times, as well as the first and last time, it has seen every process run for each endpoint and user, and then displays only those processes that you have marked as uncommon in the `uncommon_processes_default.csv` table. +confidence = medium +explanation = This search looks for applications on the endpoint that you have marked as uncommon. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Accessibility Features"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Unload Sysmon Filter Driver - Rule] type = detection asset_type = confidence = medium -explanation = This search is looking to detect execution of `fltMC.exe` that specifically used for unloading the Sysmon Filter Driver +explanation = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Disabling Security Tools"], "mitre_technique_id": ["T1089"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["DE.CM"]} known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search detects unsigned images loaded by LSASS (Local Security Authrity Subsystem Service). Normally, LSASS only loads signed images. Therefore, it is a malicious indicator when unsigned images are loaded by LSASS. This can be an indicator for credential dumping using tools like Windows Credential Editor. +explanation = This search detects loading of unsigned images by LSASS. how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +confidence = medium +explanation = This search gives you the hosts where a backup was attempted and then failed. how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = ["Netbackup"] +providing_technologies = none [savedsearch://ESCU - Unusually Long Command Line - Rule] type = detection asset_type = confidence = medium -explanation = This search calculates the average and standard deviation for the length of the command lines on each of your endpoints and alerts when it detects a command line with a length over 10 times the standard deviation larger than the average command line. +explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications start with long command lines. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule] type = detection asset_type = confidence = medium -explanation = This search leverages the Machine Learning Toolkit (MLTK) to identify outliers in the length of the command lines observed to be used by a specific user. The companion search, "Baseline of Command Line Length - MLTK," creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Unusually Long Content-Type Length - Rule] type = detection asset_type = Web Server -confidence = high -explanation = This detection search uses HTTP traffic data captured with Splunk Stream. The search is constructed to use "stream:http" sourcetype and counts of the number of times an HTTP request is received by a destination which the length of the Content-Type header value the client sends the server is greater than 100 characters long. We calculate this content_type_length field and output the results. +confidence = medium +explanation = This search looks for unusually long strings in the Content-Type http header that the client sends the server. how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters. -providing_technologies = ["Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - WMI Permanent Event Subscription - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. +explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Sysmon event ID 21. +explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI temporary event subscription by watching for Windows event ID 5860. +explanation = This search looks for the creation of WMI temporary event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Web Fraud - Account Harvesting - Rule] type = detection asset_type = Account confidence = medium -explanation = When a fraudster is setting the stage for a campaign, they will often create many user accounts on the website. This is a simple example of how to detect a many-account creation hosted on a Magento2 e-commerce platform, where the fraudster is using email addresses from a single email domain. +explanation = This search is used to identify the creation of multiple user accounts using the same email domain name. how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["DE.CM", "DE.DP"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. -providing_technologies = ["Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] type = detection asset_type = account confidence = medium -explanation = It's suspicious when someone or something is moving throughout your website too quickly or with a perfect click cadence. Fortunately, it's easy to detect by calculating the time between clicks for each session and highlighting the anomalous behavior. +explanation = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. -providing_technologies = ["Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule] type = detection asset_type = account confidence = medium -explanation = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 +explanation = This search is used to identify user accounts that share a common password. how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -providing_technologies = ["Splunk Stream"] +providing_technologies = none [savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule] type = detection asset_type = Web Server confidence = medium -explanation = This detection search uses the Enterprise Security Endpoint data model. The search uses tstats to search within an accelerated data model to find suspicious applications or processes such as whoami, ping, iptables, wget, service, or curl, running on hosts which are marked as web servers in the Assets and Identity Framework of ES. +explanation = This search looks for suspicious processes on all systems labeled as web servers. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Discovery", "System Information Discovery"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = none [savedsearch://ESCU - Windows Event Log Cleared - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at the Windows security and system event logs. EventCode 1002 in the security log indicates that the log has been cleared, EventCode 1000 in the security log indicates the event logging service has been shut down, and EventCode 104 in the system log indicates the application log has been cleared. If any of these events are found, a notable will be generated. +confidence = medium +explanation = This search looks for Windows events that indicate one of the Windows event logs has been purged. how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. -providing_technologies = ["Microsoft Windows"] +providing_technologies = none [savedsearch://ESCU - Windows hosts file modification - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. +confidence = medium +explanation = The search looks for modifications to the hosts file on all Windows endpoints across your environment. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = none ### END DETECTIONS ### -### INVESTIGATIONS ### +### RESPONSE TASKS ### [savedsearch://ESCU - AWS Investigate User Activities By ARN] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -earliest_time_offset = 72000 -latest_time_offset = 36000 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - AWS Investigate User Activities By Source User] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -2947,41 +2885,49 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - AWS Network Interface details via resourceId] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS configuration inputs -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - AWS S3 Bucket details via bucketName] type = investigation explanation = none how_to_implement = To implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and configure your AWS inputs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - All backup logs for host] type = investigation explanation = none how_to_implement = The successfully implement this search you must first send your backup logs to Splunk. -known_false_positives = None at this time -earliest_time_offset = 1209600 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Amazon EKS Kubernetes activity by src_ip] +[savedsearch://ESCU - Amazon EKS Kubernetes activity by src ip] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch EKS inputs. -known_false_positives = None at this time -earliest_time_offset = -70m@m -latest_time_offset = -10m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Analyze Malicious File] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - DNS Hijack Enrichment] type = investigation @@ -2989,39 +2935,39 @@ explanation = none how_to_implement = If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Domain Certificate Investigation] type = investigation explanation = none how_to_implement = To successfully implement this phantom playbook, you must integrate Enterprise Security with Phantom. Configure this playbook in the correlation search `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response action. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Excessive Account Lockouts Enrichment And Response] type = investigation explanation = none how_to_implement = Import playbook into phantom -known_false_positives = None at this time -earliest_time_offset = -4h@h -latest_time_offset = -5m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - GCP Kubernetes activity by src_ip] +[savedsearch://ESCU - GCP Kubernetes activity by src ip] type = investigation explanation = none how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -known_false_positives = None at this time -earliest_time_offset = -70m@m -latest_time_offset = -10m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get All AWS Activity From City] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3029,7 +2975,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3037,7 +2983,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3045,7 +2991,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3053,247 +2999,247 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = 43200 -latest_time_offset = 1 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Backup Logs For Endpoint] type = investigation explanation = none how_to_implement = You must be ingesting your backup logs. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Certificate logs for a domain] type = investigation explanation = none how_to_implement = You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations -known_false_positives = None at this time -earliest_time_offset = 36000 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get DNS Server History for a host] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get DNS traffic ratio] type = investigation explanation = none how_to_implement = You must be ingesting your network traffic -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get EC2 Instance Details by instanceId] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get EC2 Launch Details] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Email Info] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Emails From Specific Sender] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get History Of Email Sources] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 172800 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Logon Rights Modifications For User] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Notable History] type = investigation explanation = none how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Notable Info] type = investigation explanation = none how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Parent Process Info] type = investigation explanation = none how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Process File Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Info] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Information For Port Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Registry Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Responsible For The DNS Traffic] type = investigation explanation = none how_to_implement = You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Registry Activities] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Risk Modifiers For Endpoint] type = investigation explanation = none how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Risk Modifiers For User] type = investigation explanation = none how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Sysmon WMI Activity for Host] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Update Logs For Endpoint] type = investigation explanation = none how_to_implement = You need to be ingesting the update logs from your various systems. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get User Information from Identity Table] type = investigation explanation = none how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Vulnerability Logs For Endpoint] type = investigation explanation = none how_to_implement = You need to be ingesting the logs from your vulnerability scanner. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Get Web Session Information via session_id] +[savedsearch://ESCU - Get Web Session Information via session id] type = investigation explanation = none how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate AWS ECR container listing activity] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.You must also install Cloud Infrastructure Data Model. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3301,7 +3247,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3309,7 +3255,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3317,430 +3263,454 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Investigate Failed Logins for Multiple Destinations] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = -7d -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - Investigate Network Traffic From src_ip] +[savedsearch://ESCU - Investigate Network Traffic From src ip] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Okta Activity by IP Address] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Okta Activity by app] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Pass the Hash Attempts] type = investigation explanation = none how_to_implement = To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -known_false_positives = None at this time -earliest_time_offset = -24h -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Pass the Ticket Attempts] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -known_false_positives = None at this time -earliest_time_offset = -24h -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Previous Unseen User] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = -60d -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Successful Remote Desktop Authentications] type = investigation explanation = none how_to_implement = You must be populating the Authentication data model with security events from your Windows event logs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Investigate Suspicious Strings in HTTP Header] type = investigation explanation = none how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In All Cloud Regions] type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In Okta] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In Single Cloud Region] type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Web Activity From Host] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - Investigate Web Activity From src_ip] +[savedsearch://ESCU - Investigate Web Activity From src ip] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web traffic and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Web POSTs From src] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Malware Hunt and Contain] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Process Chain Analysis] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Quarantaine Infected Host] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Suspicious Email Attachment Investigate and Delete] type = investigation explanation = none how_to_implement = Synchronize the community playbook repository in Phantom, then open the playbook and follow the deployment notes to configure it for your environment. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -### END INVESTIGATIONS ### +### END RESPONSE TASKS ### ### BASELINES ### [savedsearch://ESCU - Add Prohibited Processes to Enterprise Security] type = support -explanation = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. +explanation = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. how_to_implement = This search should be run on each new install of ESCU. -known_false_positives = -providing_technologies = ["Splunk Enterprise Security"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of API Calls per User ARN] type = support -explanation = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Command Line Length - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of DNS Query Length - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel in your environment. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] type = support -explanation = Create a machine-learning (ML) model to establish a baseline for how many RunInstances users do in the environment. This can help you identify excessive numbers of RunInstances which may warrant further investigation to determine if there is misuse or abuse. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK] type = support -explanation = Create a machine-learning (ML) model to establish a baseline for how many TerminateInstances users do in the environment. This can help you identify excessive numbers of TerminateInstances which may warrant further investigation to determine if there is misuse or abuse. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Network ACL Activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of SMB Traffic - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Security Group Activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of blocked outbound traffic from AWS] type = support -explanation = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Count of Unique IPs Connecting to Ports] type = support -explanation = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. +explanation = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Count of assets by category] type = support -explanation = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. +explanation = This search shows you every asset category you have and the assets that belong to those categories. how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. -known_false_positives = -providing_technologies = ["Splunk Enterprise Security"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Create a list of approved AWS service accounts] type = support -explanation = We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to `aws_service_accounts.csv`. +explanation = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - DNSTwist Domain Names] type = support -explanation = This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\_SA\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=` +explanation = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches. how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. -known_false_positives = -providing_technologies = ["Splunk Enterprise"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Discover DNS records] type = support -explanation = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` +explanation = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -known_false_positives = Please vet the lookup created by this baseline search -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic] type = support -explanation = This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic. +explanation = This search counts the numbers of times the system has generated remote desktop traffic. how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic] type = support -explanation = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. +explanation = This search counts the numbers of times the system has created remote desktop traffic how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Using Remote Desktop] type = support -explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process name in the Endpoint data model. +explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Monitor Successful Backups] type = support -explanation = This search gives you the count and the hostname of all the systems that had a successful backup each day. +explanation = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = -providing_technologies = ["Netbackup"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Monitor Unsuccessful Backups] type = support -explanation = This search gives you the count and hostname of all the systems that had a backup failure each day +explanation = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = -providing_technologies = ["Netbackup"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Cross Account Activity] type = support -explanation = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to a lookup file. +explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Provisioning Activity Sources] type = support -explanation = This search includes any event name that begins with "run" or "create," and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a **GeoIP** database to determine the physical location of this IP address. This table outputs to a file. +explanation = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Regions] type = support -explanation = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. +explanation = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Creations By User] type = support -explanation = In this support search, we create a table of the earliest and latest time for each user that has created a cloud compute instance. +explanation = This search builds a table of previously seen users that have launched a cloud compute instance. how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Images] type = support -explanation = In this support search, we create a table of the earliest and latest time for each image id that has been seen. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen images used to launch cloud compute instances how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Instance Types] type = support -explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that the compute type has been seen in our dataset. We only look for those events where an instance has been created. All of these entries will be added to the `previously_seen_cloud_compute_instance_types` lookup file, which will act as a baseline for detections. +explanation = This search builds a table of previously seen cloud compute instance types how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Regions] type = support -explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that this region has been seen in our dataset, grouped by the region. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_cloud_regions` lookup file, which will act like a baseline for detections. +explanation = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 AMIs] type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen AMIs used to launch EC2 instances how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Instance Types] type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen EC2 instance types how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Launches By User] type = support -explanation = In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Modifications By User] type = support -explanation = In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the `ec2_modification_api_calls` macro for ease of use. This table is then outputted to a file. +explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Running Windows Services] type = support -explanation = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. In this specific log event, the `param1` field represents the "service_name" and the `param2` represents the action/status of the service. This search will create a table of the first and last time as particular Windows service was seen to be in the `running` status. +explanation = This collects the services that have been started across your entire enterprise. how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen API call per user roles in CloudTrail] type = support -explanation = In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as "userName" in the CloudTrail logs. +explanation = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen S3 bucket access by remote IP] type = support -explanation = In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the S3 bucket. +explanation = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen command line arguments] type = support -explanation = In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value. +explanation = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen users in CloudTrail] type = support -explanation = In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. +explanation = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = n/a -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch] type = support -explanation = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. +explanation = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Update previously seen users in CloudTrail] type = support -explanation = In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. +explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = n/a -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Windows Updates Install Failures] type = support -explanation = This search gives you the count of the number of systems that attempted and failed to install a Windows update each day. +explanation = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned. how_to_implement = You must be ingesting your Windows Update Logs -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Windows Updates Install Successes] type = support -explanation = This search gives you the count and name of all the systems that had a successful update applied each day +explanation = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned. how_to_implement = You must be ingesting your Windows Update Logs -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none ### END ESCU BASELINES ### \ No newline at end of file diff --git a/package/default/workflow_actions.conf b/package/default/workflow_actions.conf new file mode 100644 index 0000000000..e18c065b73 --- /dev/null +++ b/package/default/workflow_actions.conf @@ -0,0 +1,480 @@ + +[workbench_panel_aws_investigate_user_activities_by_arn]] +label = Workbench - AWS Investigate User Activities By ARN +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_aws_investigate_user_activities_by_arn&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_aws_investigate_user_activities_by_accesskeyid]] +label = Workbench - AWS Investigate User Activities By AccessKeyId +type = link +fields = *accessKeyId* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_investigate_user_activities_by_accesskeyid&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_aws_investigate_user_activities_by_source_user]] +label = Workbench - AWS Investigate User Activities By Source User +type = link +fields = *src_user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_investigate_user_activities_by_source_user&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_aws_network_acl_details_from_id]] +label = Workbench - AWS Network ACL Details from ID +type = link +fields = *networkAclId* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_network_acl_details_from_id&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_aws_network_interface_details_via_resourceid]] +label = Workbench - AWS Network Interface details via resourceId +type = link +fields = *resourceId* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_network_interface_details_via_resourceid&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_aws_s3_bucket_details_via_bucketname]] +label = Workbench - AWS S3 Bucket details via bucketName +type = link +fields = *bucketName* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_aws_s3_bucket_details_via_bucketname&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_all_backup_logs_for_host]] +label = Workbench - All backup logs for host +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_all_backup_logs_for_host&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_amazon_eks_kubernetes_activity_by_src_ip]] +label = Workbench - Amazon EKS Kubernetes activity by src ip +type = link +fields = *src_ip* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_amazon_eks_kubernetes_activity_by_src_ip&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_gcp_kubernetes_activity_by_src_ip]] +label = Workbench - GCP Kubernetes activity by src ip +type = link +fields = *src_user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_gcp_kubernetes_activity_by_src_ip&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_all_aws_activity_from_city]] +label = Workbench - Get All AWS Activity From City +type = link +fields = *City* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_all_aws_activity_from_city&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_all_aws_activity_from_country]] +label = Workbench - Get All AWS Activity From Country +type = link +fields = *Country* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_all_aws_activity_from_country&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_all_aws_activity_from_ip_address]] +label = Workbench - Get All AWS Activity From IP Address +type = link +fields = *src_ip* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_all_aws_activity_from_ip_address&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_all_aws_activity_from_region]] +label = Workbench - Get All AWS Activity From Region +type = link +fields = *Region* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_all_aws_activity_from_region&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_authentication_logs_for_endpoint]] +label = Workbench - Get Authentication Logs For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_authentication_logs_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_backup_logs_for_endpoint]] +label = Workbench - Get Backup Logs For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_backup_logs_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_certificate_logs_for_a_domain]] +label = Workbench - Get Certificate logs for a domain +type = link +fields = *domain* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_certificate_logs_for_a_domain&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_dns_server_history_for_a_host]] +label = Workbench - Get DNS Server History for a host +type = link +fields = *src_ip* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_dns_server_history_for_a_host&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + + +[workbench_panel_get_ec2_instance_details_by_instanceid]] +label = Workbench - Get EC2 Instance Details by instanceId +type = link +fields = *instanceId* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_ec2_instance_details_by_instanceid&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_ec2_launch_details]] +label = Workbench - Get EC2 Launch Details +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_ec2_launch_details&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_email_info]] +label = Workbench - Get Email Info +type = link +fields = *message_id* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_email_info&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_emails_from_specific_sender]] +label = Workbench - Get Emails From Specific Sender +type = link +fields = *src_user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_emails_from_specific_sender&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address]] +label = Workbench - Get First Occurrence and Last Occurrence of a MAC Address +type = link +fields = *src_mac* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_history_of_email_sources]] +label = Workbench - Get History Of Email Sources +type = link +fields = *src* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_history_of_email_sources&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_logon_rights_modifications_for_endpoint]] +label = Workbench - Get Logon Rights Modifications For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_logon_rights_modifications_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_logon_rights_modifications_for_user]] +label = Workbench - Get Logon Rights Modifications For User +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_get_logon_rights_modifications_for_user&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_notable_history]] +label = Workbench - Get Notable History +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_notable_history&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_notable_info]] +label = Workbench - Get Notable Info +type = link +fields = *event_id* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_notable_info&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + + + + + + + +[workbench_panel_get_process_responsible_for_the_dns_traffic]] +label = Workbench - Get Process Responsible For The DNS Traffic +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_process_responsible_for_the_dns_traffic&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_registry_activities]] +label = Workbench - Get Registry Activities +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_registry_activities&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_risk_modifiers_for_endpoint]] +label = Workbench - Get Risk Modifiers For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_risk_modifiers_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_risk_modifiers_for_user]] +label = Workbench - Get Risk Modifiers For User +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_get_risk_modifiers_for_user&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + + +[workbench_panel_get_update_logs_for_endpoint]] +label = Workbench - Get Update Logs For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_update_logs_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_user_information_from_identity_table]] +label = Workbench - Get User Information from Identity Table +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_get_user_information_from_identity_table&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_vulnerability_logs_for_endpoint]] +label = Workbench - Get Vulnerability Logs For Endpoint +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_vulnerability_logs_for_endpoint&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_get_web_session_information_via_session_id]] +label = Workbench - Get Web Session Information via session id +type = link +fields = *session_id* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_get_web_session_information_via_session_id&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_aws_ecr_container_listing_activity]] +label = Workbench - Investigate AWS ECR container listing activity +type = link +fields = *Compute.event_name* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_aws_ecr_container_listing_activity&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_aws_user_activities_by_user_field]] +label = Workbench - Investigate AWS User Activities by user field +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_investigate_aws_user_activities_by_user_field&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_aws_activities_via_region_name]] +label = Workbench - Investigate AWS activities via region name +type = link +fields = *awsRegion* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_aws_activities_via_region_name&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_cloud_compute_instance_activities]] +label = Workbench - Investigate Cloud Compute Instance Activities +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_cloud_compute_instance_activities&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_failed_logins_for_multiple_destinations]] +label = Workbench - Investigate Failed Logins for Multiple Destinations +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_failed_logins_for_multiple_destinations&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_network_traffic_from_src_ip]] +label = Workbench - Investigate Network Traffic From src ip +type = link +fields = *src_ip* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_network_traffic_from_src_ip&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_okta_activity_by_ip_address]] +label = Workbench - Investigate Okta Activity by IP Address +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_investigate_okta_activity_by_ip_address&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_okta_activity_by_app]] +label = Workbench - Investigate Okta Activity by app +type = link +fields = *app* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_okta_activity_by_app&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_pass_the_hash_attempts]] +label = Workbench - Investigate Pass the Hash Attempts +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_pass_the_hash_attempts&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_pass_the_ticket_attempts]] +label = Workbench - Investigate Pass the Ticket Attempts +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_pass_the_ticket_attempts&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_previous_unseen_user]] +label = Workbench - Investigate Previous Unseen User +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_previous_unseen_user&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_successful_remote_desktop_authentications]] +label = Workbench - Investigate Successful Remote Desktop Authentications +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_successful_remote_desktop_authentications&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + + + +[workbench_panel_investigate_user_activities_in_okta]] +label = Workbench - Investigate User Activities In Okta +type = link +fields = *user* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_identity=$@field_value$&panel=workbench_panel_investigate_user_activities_in_okta&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + + +[workbench_panel_investigate_web_activity_from_host]] +label = Workbench - Investigate Web Activity From Host +type = link +fields = *dest* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_web_activity_from_host&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_web_activity_from_src_ip]] +label = Workbench - Investigate Web Activity From src ip +type = link +fields = *src_ip* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_web_activity_from_src_ip&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_investigate_web_posts_from_src]] +label = Workbench - Investigate Web POSTs From src +type = link +fields = *src* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_investigate_web_posts_from_src&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + +[workbench_panel_process_chain_analysis]] +label = Workbench - Process Chain Analysis +type = link +fields = *process_guid* +display_location = field_menu +link.uri = /app/$@namespace$/ess_workbench_panel?type_asset=$@field_value$&panel=workbench_panel_process_chain_analysis&drilldown_field=$@field_name$&use_drilldown_time=true +link.target = blank +link.method = get + diff --git a/requirements.txt b/requirements.txt index 190c308843..7964b28e3c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,24 +1,37 @@ +antlr4-python3-runtime==4.8 aspy.yaml==1.3.0 +attackcti==0.3.2 attrs==19.3.0 +certifi==2020.4.5.1 cfgv==3.1.0 +chardet==3.0.4 configparser==5.0.0 contextlib2==0.6.0.post1 -identify==1.4.16 +identify==1.4.17 +idna==2.9 importlib-metadata==1.6.0 importlib-resources==1.5.0 Jinja2==2.11.2 jsonschema==3.2.0 MarkupSafe==1.1.1 -more-itertools==8.2.0 +more-itertools==8.3.0 nodeenv==1.3.5 pathlib2==2.3.5 -pre-commit==2.3.0 -PyYAML==5.3.1 +pre-commit==2.4.0 pyrsistent==0.16.0 +python-dateutil==2.8.1 +pytz==2020.1 +PyYAML==5.3.1 +requests==2.23.0 scandir==1.10.0 semantic-version==2.8.5 -six==1.14.0 +simplejson==3.17.0 +six==1.15.0 +stix2==1.4.0 +stix2-patterns==1.3.0 +taxii2-client==2.0.0 toml==0.10.0 typing==3.7.4.1 -virtualenv==20.0.20 +urllib3==1.25.9 +virtualenv==20.0.21 zipp==3.1.0 diff --git a/response_tasks/all_backup_logs_for_host.yml b/response_tasks/all_backup_logs_for_host.yml new file mode 100644 index 0000000000..412f36e5fa --- /dev/null +++ b/response_tasks/all_backup_logs_for_host.yml @@ -0,0 +1,15 @@ +name: All backup logs for host +id: bc91a8cf-aaaa-4bb2-8140-e756cc06fd72 +version: 1 +date: '2017-09-12' +description: Retrieve the backup logs for the last 2 weeks for a specific host in + order to investigate why backups are not completing successfully. +how_to_implement: The successfully implement this search you must first send your + backup logs to Splunk. +author: Rico Valdez, Splunk +inputs: +- dest +search: '| search sourcetype="netbackup_logs" dest=$dest$' +tags: + analytics_story: + - Monitor Backup Solution diff --git a/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml b/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml new file mode 100644 index 0000000000..9c86ab624a --- /dev/null +++ b/response_tasks/amazon_eks_kubernetes_activity_by_src_ip.yml @@ -0,0 +1,20 @@ +name: Amazon EKS Kubernetes activity by src ip +id: a636cca4-7434-4a15-a278-c70734938e39 +version: 1 +date: '2020-04-13' +description: This search provides investigation data about requests via user agent, + authentication request URI, verb and cluster name data against Kubernetes cluster + from a specific IP address +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch + EKS inputs. +author: Rod Soto, Splunk +inputs: +- src_ip +search: sourcetype="aws:cloudwatchlogs:eks" sourceIPs{}=$src_ip$|rename sourceIPs{} + as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(user.username) + values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision + src_ip +tags: + analytics_story: + - Kubernetes Scanning Activity diff --git a/response_tasks/analyze_malicious_file.yml b/response_tasks/analyze_malicious_file.yml new file mode 100644 index 0000000000..0a606082e7 --- /dev/null +++ b/response_tasks/analyze_malicious_file.yml @@ -0,0 +1,6 @@ +name: Analyze Malicious File +id: 6ee5c067-8228-4926-abb2-54f2c59d726e +version: 1 +date: '2020-04-29' +description: Perform a static and dynamic malware analysis for the malicious file. Use the findings for further response tasks. +author: Patrick Bareiss, Splunk diff --git a/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml b/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml new file mode 100644 index 0000000000..716351370d --- /dev/null +++ b/response_tasks/aws_investigate_user_activities_by_accesskeyid.yml @@ -0,0 +1,18 @@ +name: AWS Investigate User Activities By AccessKeyId +id: 703b65a4-a0ae-4171-965d-45507506c64f +version: 1 +date: '2018-06-08' +description: This search retrieves the times, ARN, source IPs, AWS regions, event + names, and the result of the event for specific credentials. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +inputs: +- accessKeyId +search: '| search sourcetype=aws:cloudtrail userIdentity.accessKeyId=$accessKeyId$ + | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | + table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage' +tags: + analytics_story: + - AWS Cross Account Activity diff --git a/response_tasks/aws_investigate_user_activities_by_arn.yml b/response_tasks/aws_investigate_user_activities_by_arn.yml new file mode 100644 index 0000000000..a6550aedde --- /dev/null +++ b/response_tasks/aws_investigate_user_activities_by_arn.yml @@ -0,0 +1,28 @@ +name: AWS Investigate User Activities By ARN +id: bc91a8cd-35e7-4bb2-6140-e756cc46fd71 +version: 2 +date: '2019-04-30' +description: This search lists all the logged CloudTrail activities by a specific + user ARN and will create a table containing the source of the user, the region of + the activity, the name and type of the event, the action taken, and all the user's + identity information. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +inputs: +- user +search: '| search sourcetype=aws:cloudtrail userIdentity.arn=$user$ | table _time + userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion + eventName eventType' +tags: + analytics_story: + - AWS Cryptomining + - AWS Network ACL Activity + - Cloud Cryptomining + - Command and Control + - Suspicious AWS EC2 Activities + - Suspicious AWS Login Activities + - Suspicious AWS S3 Activities + - Suspicious AWS Traffic + - Unusual AWS EC2 Modifications diff --git a/response_tasks/aws_investigate_user_activities_by_source_user.yml b/response_tasks/aws_investigate_user_activities_by_source_user.yml new file mode 100644 index 0000000000..cd1083288b --- /dev/null +++ b/response_tasks/aws_investigate_user_activities_by_source_user.yml @@ -0,0 +1,18 @@ +name: AWS Investigate User Activities By Source User +id: b0d2e6a8-75fa-4b1b-9486-3d32acadf891 +version: 1 +date: '2018-06-08' +description: This search retrieves the times, ARN, source IPs, AWS regions, event + names, and the result of the event for specific ARNs. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +inputs: +- src_user +search: '| search sourcetype=aws:cloudtrail userIdentity.arn=$src_user$ | spath output=user + path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, + awsRegion, eventName, errorCode, errorMessage' +tags: + analytics_story: + - AWS Cross Account Activity diff --git a/response_tasks/aws_network_acl_details_from_id.yml b/response_tasks/aws_network_acl_details_from_id.yml new file mode 100644 index 0000000000..a6e73d4846 --- /dev/null +++ b/response_tasks/aws_network_acl_details_from_id.yml @@ -0,0 +1,19 @@ +name: AWS Network ACL Details from ID +id: f3fb4d1b-5f33-4b01-b541-c7ah9534c242 +version: 1 +date: '2017-01-22' +description: This search queries AWS description logs and returns all the information + about a specific network ACL via network ACL ID +how_to_implement: In order to implement this search, you must install the AWS App + for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) + and configure your AWS description inputs. +author: Bhavin Patel, Splunk +inputs: +- networkAclId +search: '| search sourcetype=aws:description id=$networkAclId$ | table id account_id + vpc_id network_acl_entries{}.*' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - Suspicious AWS Traffic diff --git a/response_tasks/aws_network_interface_details_via_resourceid.yml b/response_tasks/aws_network_interface_details_via_resourceid.yml new file mode 100644 index 0000000000..d7b216c3ff --- /dev/null +++ b/response_tasks/aws_network_interface_details_via_resourceid.yml @@ -0,0 +1,22 @@ +name: AWS Network Interface details via resourceId +id: f3fb4d1c-5f33-4b01-b541-c3ah9534c241 +version: 1 +date: '2018-05-07' +description: This search queries AWS configuration logs and returns the information + about a specific network interface via network interface ID. The information will + include the ARN of the network interface, its relationships with other AWS resources, + the public and the private IP associated with the network interface. +how_to_implement: In order to implement this search, you must install the AWS App + for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) + and configure your AWS configuration inputs +author: Bhavin Patel, Splunk +inputs: +- resourceId +search: '| search sourcetype=aws:config resourceId=$resourceId$ | table _time ARN + relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress + configuration.privateIpAddresses{}.association.publicIp' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - Suspicious AWS Traffic diff --git a/response_tasks/aws_s3_bucket_details_via_bucketname.yml b/response_tasks/aws_s3_bucket_details_via_bucketname.yml new file mode 100644 index 0000000000..984bc003d1 --- /dev/null +++ b/response_tasks/aws_s3_bucket_details_via_bucketname.yml @@ -0,0 +1,20 @@ +name: AWS S3 Bucket details via bucketName +id: f3fb2q1c-5f33-4b01-b541-c2ah9534c242 +version: 1 +date: '2018-06-26' +description: This search queries AWS configuration logs and returns the information + about a specific S3 bucket. The information returned includes the time the S3 bucket + was created, the resource ID, the region it belongs to, the value of action performed, + AWS account ID, and configuration values of the access-control lists associated + with the bucket. +how_to_implement: To implement this search, you must install the AWS App for Splunk + (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and + configure your AWS inputs. +author: Bhavin Patel, Splunk +inputs: +- bucketName +search: '| search sourcetype=aws:config resourceId=$bucketName$ | table resourceCreationTime + resourceId awsRegion action aws_account_id supplementaryConfiguration.AccessControlList' +tags: + analytics_story: + - Suspicious AWS S3 Activities diff --git a/investigations/dns_hijack_enrichment.yml b/response_tasks/dns_hijack_enrichment.yml similarity index 71% rename from investigations/dns_hijack_enrichment.yml rename to response_tasks/dns_hijack_enrichment.yml index 19976edfd4..98419a7c15 100644 --- a/investigations/dns_hijack_enrichment.yml +++ b/response_tasks/dns_hijack_enrichment.yml @@ -1,12 +1,7 @@ -creation_date: '2017-04-10' -data_metadata: - data_source: - - Censys - - Passive Total - - WHOIS - providing_technologies: - - Splunk Stream - - Bro +name: DNS Hijack Enrichment +id: c096f721-8842-42ce-bfc7-74bd9a72c712 +version: 1 +date: '2019-02-14' description: 'This Playbook is part of the Splunk Analytic Story called DNS Hijacking. It is made to be run when the Detection Search within that story called "DNS Record Changed" is used to identify DNS record changes for cloud and corporate domains @@ -31,27 +26,8 @@ how_to_implement: 'If Splunk>Phantom is also configured in your environment, a P (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ ' -id: c096f721-8842-42ce-bfc7-74bd9a72c712 -investigate: - phantom: - phantom_server: automation (hostname) - playbook_name: community/dns_hijack_enrichment - playbook_url: https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/ - schedule: - earliest_time: "7200" - latest_time: "0" - sensitivity: green - severity: medium -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2019-02-14' -name: DNS Hijack Enrichment -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: phantom -version: '1.0' +author: Bhavin Patel, Splunk +playbook: + name: dns_hijack_enrichment + url_json: https://github.com/phantomcyber/playbooks/blob/4.6/dns_hijack_enrichment.json + url_python: https://github.com/phantomcyber/playbooks/blob/4.6/dns_hijack_enrichment.py diff --git a/investigations/domain_cert_investigation.yml b/response_tasks/domain_certificate_investigation.yml similarity index 57% rename from investigations/domain_cert_investigation.yml rename to response_tasks/domain_certificate_investigation.yml index 6910a196a3..e5c0e3905f 100644 --- a/investigations/domain_cert_investigation.yml +++ b/response_tasks/domain_certificate_investigation.yml @@ -1,9 +1,7 @@ -creation_date: '2019-04-29' -data_metadata: - data_source: - - Censys - providing_technologies: - - Censys +name: Domain Certificate Investigation +id: c096f721-8842-42ce-2fc7-742d8272b712 +version: 1 +date: '2019-04-29' description: Investigate domain names and URLs of a potentially malicious website. These domain names and URLs could come from anywhere, but this Playbook was designed to work with the Splunk Analytic Story focused on evilginx2 phishing techniques @@ -18,34 +16,8 @@ how_to_implement: To successfully implement this phantom playbook, you must inte Enterprise Security with Phantom. Configure this playbook in the correlation search `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response action. -id: c096f721-8842-42ce-2fc7-742d8272b712 -investigate: - phantom: - phantom_server: automation (hostname) - playbook_name: community/lets_encrypt_domain_investigate - playbook_url: https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/ - schedule: - cron_schedule: 0 * * * * - earliest_time: '0' - latest_time: '86400' - sensitivity: green - severity: medium -maintainers: - - company: Splunk - email: rnolen@splunk.com - name: 'Russell Nolen ' - - company: Splunk - email: proyer@splunk.com - name: Philip Royer -modification_date: '2019-04-29' -name: Domain Certificate Investigation -original_authors: - - company: Splunk - email: rnolen@splunk.com - name: 'Russell Nolen ' - - company: Splunk - email: proyer@splunk.com - name: Philip Royer -spec_version: 2 -type: phantom -version: '1.0' +author: Philip Royer, Splunk +playbook: + name: lets_encrypt_domain_investigate + url_json: https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json + url_python: https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py diff --git a/investigations/excessive_account_lockouts_enrichment.yml b/response_tasks/excessive_account_lockouts_enrichment_and_response.yml similarity index 55% rename from investigations/excessive_account_lockouts_enrichment.yml rename to response_tasks/excessive_account_lockouts_enrichment_and_response.yml index eec5119daa..c471e420cd 100644 --- a/investigations/excessive_account_lockouts_enrichment.yml +++ b/response_tasks/excessive_account_lockouts_enrichment_and_response.yml @@ -1,11 +1,7 @@ -creation_date: '2017-08-17' -data_metadata: - data_models: - - Change - data_source: - - Windows Event Logs - providing_technologies: - - Microsoft Windows +name: Excessive Account Lockouts Enrichment And Response +id: ab62b5c1-95d4-4e71-8fd7-53a55db33da4 +version: 2 +date: '2019-02-14' description: This Playbook is part of the Splunk Analytic Story called Account Monitoring and Controls. It is made to be run when the Detection Search within that story called "Detect Excessive Account Lockouts From Endpoint" is used to identify a potential @@ -17,28 +13,8 @@ description: This Playbook is part of the Splunk Analytic Story called Account M to login to the source of the lockouts, gather more information, and allow Phantom to shutdown the server after prompting an analyst or responder. how_to_implement: Import playbook into phantom -id: ab62b5c1-95d4-4e71-8fd7-53a55db33da4 -investigate: - phantom: - phantom_server: automation (hostname) - playbook_name: community/excessive_account_lockouts_enrichment_and_response - playbook_url: https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/ - schedule: - cron_schedule: 0 * * * * - earliest_time: -4h@h - latest_time: -5m@m - sensitivity: green - severity: medium -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2019-02-14' -name: Excessive Account Lockouts Enrichment And Response -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -spec_version: 2 -type: phantom -version: '2.0' +author: Bhavin Patel, Splunk +playbook: + name: excessive_account_lockouts_enrichment_and_response + url_json: https://github.com/phantomcyber/playbooks/blob/4.6/excessive_account_lockouts_enrichment_and_response.json + url_python: https://github.com/phantomcyber/playbooks/blob/4.6/excessive_account_lockouts_enrichment_and_response.py diff --git a/response_tasks/gcp_kubernetes_activity_by_src_ip.yml b/response_tasks/gcp_kubernetes_activity_by_src_ip.yml new file mode 100644 index 0000000000..555a926e7c --- /dev/null +++ b/response_tasks/gcp_kubernetes_activity_by_src_ip.yml @@ -0,0 +1,24 @@ +name: GCP Kubernetes activity by src ip +id: c00e7626-92cc-4e06-9a51-b6db0a50bd1f +version: 1 +date: '2020-04-13' +description: This search provides investigation data about requests via user agent, + authentication request URI, resource path and cluster name data against Kubernetes + cluster from a specific IP address +how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), + then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. + You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection + to filter out FPs. +author: Rod Soto, Splunk +inputs: +- src_user +search: sourcetype="google:gcp:pubsub:message" data.protoPayload.requestMetadata.callerIp={src_ip} + | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) + as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names + values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) + as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as + user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name + data.resource.type +tags: + analytics_story: + - Kubernetes Scanning Activity diff --git a/response_tasks/get_all_aws_activity_from_city.yml b/response_tasks/get_all_aws_activity_from_city.yml new file mode 100644 index 0000000000..5724dcaf9d --- /dev/null +++ b/response_tasks/get_all_aws_activity_from_city.yml @@ -0,0 +1,22 @@ +name: Get All AWS Activity From City +id: 0abeeb40-1255-4b68-91d1-7a7eb410c4b8 +version: 1 +date: '2018-03-19' +description: This search retrieves all the activity from a specific city and will + create a table containing the time, city, ARN, username, the type of user, the source + IP address, the AWS region the activity was in, the API called, and whether or not + the API call was successful. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +inputs: +- City +search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search + City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName + path=userIdentity.userName | spath output=userType path=userIdentity.type | rename + sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, + awsRegion, eventName, errorCode' +tags: + analytics_story: + - AWS Suspicious Provisioning Activities diff --git a/response_tasks/get_all_aws_activity_from_country.yml b/response_tasks/get_all_aws_activity_from_country.yml new file mode 100644 index 0000000000..beb035556c --- /dev/null +++ b/response_tasks/get_all_aws_activity_from_country.yml @@ -0,0 +1,22 @@ +name: Get All AWS Activity From Country +id: e763cdb9-00da-41e0-9bda-444debc9501a +version: 1 +date: '2018-03-19' +description: This search retrieves all the activity from a specific country and will + create a table containing the time, country, ARN, username, the type of user, the + source IP address, the AWS region the activity was in, the API called, and whether + or not the API call was successful. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +inputs: +- Country +search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search + Country=$Country$ | spath output=user path=userIdentity.arn | spath output=awsUserName + path=userIdentity.userName | spath output=userType path=userIdentity.type | rename + sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, + awsRegion, eventName, errorCode' +tags: + analytics_story: + - AWS Suspicious Provisioning Activities diff --git a/response_tasks/get_all_aws_activity_from_ip_address.yml b/response_tasks/get_all_aws_activity_from_ip_address.yml new file mode 100644 index 0000000000..270ec77de0 --- /dev/null +++ b/response_tasks/get_all_aws_activity_from_ip_address.yml @@ -0,0 +1,26 @@ +name: Get All AWS Activity From IP Address +id: 446ec87a-85c6-40d4-b060-bea4498281d6 +version: 1 +date: '2018-03-19' +description: This search retrieves all the activity from a specific IP address and + will create a table containing the time, ARN, username, the type of user, the IP + address, the AWS region the activity was in, the API called, and whether or not + the API call was successful. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +inputs: +- src_ip +search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search + sourceIPAddress=$src_ip$ | spath output=user path=userIdentity.arn | spath output=awsUserName + path=userIdentity.userName | spath output=userType path=userIdentity.type | rename + sourceIPAddress as src_ip | table _time, user, userName, userType, src_ip, awsRegion, + eventName, errorCode' +tags: + analytics_story: + - AWS Network ACL Activity + - AWS Suspicious Provisioning Activities + - Command and Control + - Suspicious AWS S3 Activities + - Suspicious AWS Traffic diff --git a/response_tasks/get_all_aws_activity_from_region.yml b/response_tasks/get_all_aws_activity_from_region.yml new file mode 100644 index 0000000000..8a0a73b1a3 --- /dev/null +++ b/response_tasks/get_all_aws_activity_from_region.yml @@ -0,0 +1,22 @@ +name: Get All AWS Activity From Region +id: 5b794bef-1743-4f6f-804a-43915a2702ff +version: 1 +date: '2018-03-19' +description: This search retrieves all the activity from a specific geographic region + and will create a table containing the time, geographic region, ARN, username, the + type of user, the source IP address, the AWS region the activity was in, the API + called, and whether or not the API call was successful. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: David Dorsey, Splunk +inputs: +- Region +search: '| search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search + Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName + path=userIdentity.userName | spath output=userType path=userIdentity.type | rename + sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, + awsRegion, eventName, errorCode' +tags: + analytics_story: + - AWS Suspicious Provisioning Activities diff --git a/response_tasks/get_authentication_logs_for_endpoint.yml b/response_tasks/get_authentication_logs_for_endpoint.yml new file mode 100644 index 0000000000..7c8c273710 --- /dev/null +++ b/response_tasks/get_authentication_logs_for_endpoint.yml @@ -0,0 +1,56 @@ +name: Get Authentication Logs For Endpoint +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd76 +version: 1 +date: '2017-11-01' +description: This search returns all users that have attempted to access a particular + endpoint. +how_to_implement: To successfully implement this search you need to be ingesting authentication + logs from your various systems and populating the Authentication data model. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| tstats count from datamodel=Authentication where Authentication.dest=$dest$ + by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action + | `drop_dm_object_name("Authentication")`' +tags: + analytics_story: + - AWS Network ACL Activity + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Brand Monitoring + - ColdRoot MacOS RAT + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - Lateral Movement + - Malicious PowerShell + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SQL Injection + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_backup_logs_for_endpoint.yml b/response_tasks/get_backup_logs_for_endpoint.yml new file mode 100644 index 0000000000..30d16c96e0 --- /dev/null +++ b/response_tasks/get_backup_logs_for_endpoint.yml @@ -0,0 +1,16 @@ +name: Get Backup Logs For Endpoint +id: fdcfb369-1725-4c24-824a-22972d7f0d44 +version: 1 +date: '2017-09-14' +description: This search will tell you the backup status from your netbackup_logs + of a specific endpoint for the last week. +how_to_implement: You must be ingesting your backup logs. +author: David Dorsey, Splunk +inputs: +- dest +search: '| search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME + as dest, MESSAGE as signature | table _time, dest, signature' +tags: + analytics_story: + - Ransomware + - SamSam Ransomware diff --git a/response_tasks/get_certificate_logs_for_a_domain.yml b/response_tasks/get_certificate_logs_for_a_domain.yml new file mode 100644 index 0000000000..a0838f51f4 --- /dev/null +++ b/response_tasks/get_certificate_logs_for_a_domain.yml @@ -0,0 +1,22 @@ +name: Get Certificate logs for a domain +id: bc91a8cf-35e7-4bb2-2240-e756cc06fd73 +version: 2 +date: '2019-04-29' +description: This search queries the Certificates datamodel and give you all the information + for a specific domain. Please note that the certificates issued by "Let's Encrypt" + are widely used by attackers. +how_to_implement: You must be ingesting your certificates or SSL logs from your network + traffic into your Certificates datamodel. Please note the wildcard(*) before domain + in the search syntax, we use to match for all domain and subdomain combinations +author: Bhavin Patel, Splunk +inputs: +- domain +search: '| tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime + FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$ by + All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name + All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash | `drop_dm_object_name(All_Certificates)` + | `drop_dm_object_name(SSL)` | rename ssl_subject_common_name as domain | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`' +tags: + analytics_story: + - Common Phishing Frameworks diff --git a/response_tasks/get_dns_server_history_for_a_host.yml b/response_tasks/get_dns_server_history_for_a_host.yml new file mode 100644 index 0000000000..c392604282 --- /dev/null +++ b/response_tasks/get_dns_server_history_for_a_host.yml @@ -0,0 +1,28 @@ +name: Get DNS Server History for a host +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd72 +version: 1 +date: '2017-11-09' +description: While investigating any detections it is important to understand which + and how many DNS servers a host has connected to in the past. This search uses data + that is tagged as DNS and gives you a count and list of DNS servers that a particular + host has connected to the previous 24 hours. +how_to_implement: To successfully implement this search, you must be ingesting your + DNS traffic +author: Bhavin Patel, Splunk +inputs: +- src_ip +search: '| search tag=dns src_ip=$src_ip$ dest_port=53 | streamstats time_window=1d + count values(dest_ip) as dcip by src_ip | table date_mday src_ip dcip count | sort + -count' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - DNS Hijacking + - Data Protection + - Dynamic DNS + - Hidden Cobra Malware + - Host Redirection + - Prohibited Traffic Allowed or Protocol Mismatch + - Suspicious AWS Traffic + - Suspicious DNS Traffic diff --git a/response_tasks/get_dns_traffic_ratio.yml b/response_tasks/get_dns_traffic_ratio.yml new file mode 100644 index 0000000000..2846f5b464 --- /dev/null +++ b/response_tasks/get_dns_traffic_ratio.yml @@ -0,0 +1,27 @@ +name: Get DNS traffic ratio +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd73 +version: 1 +date: '2017-11-09' +description: 'This search calculates the ratio of DNS traffic originating and coming + from a host to a list of DNS servers over the last 24 hours. A high value of this + ratio could be very useful to quickly understand if a src_ip (host) is sending a + high volume of data out via port 53, could be an indicator of data exfiltration + via DNS. ' +how_to_implement: You must be ingesting your network traffic +author: Bhavin Patel, Splunk +inputs: +- src_ip +- dest_ip +search: '| tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" + sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic + All_Traffic.dest_port=53 All_Traffic.src=$src_ip$ All_Traffic.dest=$dest_ip$ | eval + ratio = (bytes_out/bytes_in) | table ratio' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - Data Protection + - Dynamic DNS + - Hidden Cobra Malware + - Suspicious AWS Traffic + - Suspicious DNS Traffic diff --git a/response_tasks/get_ec2_instance_details_by_instanceid.yml b/response_tasks/get_ec2_instance_details_by_instanceid.yml new file mode 100644 index 0000000000..b28bdcedbe --- /dev/null +++ b/response_tasks/get_ec2_instance_details_by_instanceid.yml @@ -0,0 +1,26 @@ +name: Get EC2 Instance Details by instanceId +id: f3db4d1b-5f33-4b01-c541-c7ah9514c242 +version: 1 +date: '2018-02-12' +description: This search queries AWS description logs and returns all the information + about a specific instance via the instanceId field +how_to_implement: In order to implement this search, you must install the AWS App + for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) + and configure your AWS description inputs. +author: Bhavin Patel, Splunk +inputs: +- instanceId +search: '| search sourcetype="aws:description" source="*:ec2_instances"| dedup id + sortby -_time | search id=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," + = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table + id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, + state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, + instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time + as "Launch Time", placement as "Availability Zone", state as State, subnet_id as + Subnet, "tags.Name" as Name, vpc_id as VPC' +tags: + analytics_story: + - AWS Cryptomining + - Cloud Cryptomining + - Suspicious AWS EC2 Activities + - Unusual AWS EC2 Modifications diff --git a/response_tasks/get_ec2_launch_details.yml b/response_tasks/get_ec2_launch_details.yml new file mode 100644 index 0000000000..3736eabbdc --- /dev/null +++ b/response_tasks/get_ec2_launch_details.yml @@ -0,0 +1,22 @@ +name: Get EC2 Launch Details +id: 0e40fe83-3edb-4d86-8206-8fed36529ca6 +version: 1 +date: '2018-03-12' +description: This search returns some of the launch details for a EC2 instance. +how_to_implement: In order to implement this search, you must install the AWS App + for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) + and configure your AWS description inputs. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId=$dest$ + |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId + as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, + responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture + as architecture, responseElements.instancesSet.items{}.keyName as keyName | table + arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName' +tags: + analytics_story: + - AWS Cryptomining + - Cloud Cryptomining + - Suspicious AWS EC2 Activities diff --git a/response_tasks/get_email_info.yml b/response_tasks/get_email_info.yml new file mode 100644 index 0000000000..48e051569b --- /dev/null +++ b/response_tasks/get_email_info.yml @@ -0,0 +1,16 @@ +name: Get Email Info +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd75 +version: 1 +date: '2017-11-09' +description: This search returns all the information Splunk might have collected a + specific email message over the last 2 hours. +how_to_implement: To successfully implement this search you must be ingesting your + email logs or capturing unencrypted network traffic which contains email communications. +author: Bhavin Patel, Splunk +inputs: +- message_id +search: '| from datamodel Email.All_Email | search message_id=$message_id$' +tags: + analytics_story: + - Brand Monitoring + - Suspicious Emails diff --git a/response_tasks/get_emails_from_specific_sender.yml b/response_tasks/get_emails_from_specific_sender.yml new file mode 100644 index 0000000000..59d4592c35 --- /dev/null +++ b/response_tasks/get_emails_from_specific_sender.yml @@ -0,0 +1,18 @@ +name: Get Emails From Specific Sender +id: 5df39b3f-447d-4869-b673-8f45ad4616fe +version: 1 +date: '2017-11-09' +description: This search returns all the emails from a specific sender over the last + 24 and next hours. +how_to_implement: To successfully implement this search you must ingest your email + logs or capture unencrypted email communications within network traffic, and populate + the Email data model. +author: David Dorsey, Splunk +inputs: +- src_user +search: '| from datamodel Email.All_Email | search src_user=$src_user$' +tags: + analytics_story: + - Brand Monitoring + - Suspicious Emails + - Web Fraud Detection diff --git a/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml b/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml new file mode 100644 index 0000000000..351dc4c90b --- /dev/null +++ b/response_tasks/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml @@ -0,0 +1,20 @@ +name: Get First Occurrence and Last Occurrence of a MAC Address +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd33 +version: 1 +date: '2017-09-13' +description: This search allows you to gather more context around a notable which + has detected a new device connecting to your network. Use this search to determine + the first and last occurrences of the suspicious device attempting to connect with + your network. +how_to_implement: To successfully implement this search, you must be ingesting the + logs from your DHCP server. +author: Bhavin Patel, Splunk +inputs: +- src_mac +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST + All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user + | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`' +tags: + analytics_story: + - Asset Tracking diff --git a/response_tasks/get_history_of_email_sources.yml b/response_tasks/get_history_of_email_sources.yml new file mode 100644 index 0000000000..1a4ed12369 --- /dev/null +++ b/response_tasks/get_history_of_email_sources.yml @@ -0,0 +1,17 @@ +name: Get History Of Email Sources +id: ddc7af28-c34d-4392-af93-7f29a4e8806c +version: 1 +date: '2019-02-21' +description: This search returns a list of all email sources seen in the 48 hours + prior to the notable event to 24 hours after, and the number of emails from each + source. +how_to_implement: To successfully implement this search you must ingest your email + logs or capture unencrypted email communications within network traffic, and populate + the Email data model. +author: Rico Valdez, Splunk +inputs: +- src +search: '|tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) + as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email + by All_Email.src |`drop_dm_object_name(All_Email)` | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`' diff --git a/response_tasks/get_logon_rights_modifications_for_endpoint.yml b/response_tasks/get_logon_rights_modifications_for_endpoint.yml new file mode 100644 index 0000000000..8bf608d76d --- /dev/null +++ b/response_tasks/get_logon_rights_modifications_for_endpoint.yml @@ -0,0 +1,17 @@ +name: Get Logon Rights Modifications For Endpoint +id: 03bffe94-ec7a-4cbe-b677-6af40d1c4505 +version: 2 +date: '2017-09-12' +description: This search allows you to retrieve any modifications to logon rights + associated with a specific host. +how_to_implement: To successfully implement this search you must be ingesting your + Windows event logs +author: David Dorsey, Splunk +inputs: +- dest +search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) + dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", + Access_Right, signature' +tags: + analytics_story: + - Account Monitoring and Controls diff --git a/response_tasks/get_logon_rights_modifications_for_user.yml b/response_tasks/get_logon_rights_modifications_for_user.yml new file mode 100644 index 0000000000..9118a6df0f --- /dev/null +++ b/response_tasks/get_logon_rights_modifications_for_user.yml @@ -0,0 +1,17 @@ +name: Get Logon Rights Modifications For User +id: 552bc86c-f72c-4d44-b3f2-06ede13af7bb +version: 2 +date: '2019-02-27' +description: This search allows you to retrieve any modifications to logon rights + for a specific user account. +how_to_implement: To successfully implement this search you must be ingesting your + Windows event logs +author: David Dorsey, Splunk +inputs: +- user +search: '| search eventtype=wineventlog_security (signature_id=4718 OR signature_id=4717) + user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", + Access_Right, signature' +tags: + analytics_story: + - Account Monitoring and Controls diff --git a/response_tasks/get_notable_history.yml b/response_tasks/get_notable_history.yml new file mode 100644 index 0000000000..6a7a5bb371 --- /dev/null +++ b/response_tasks/get_notable_history.yml @@ -0,0 +1,73 @@ +name: Get Notable History +id: 3d6c3213-5fff-4a1e-b57d-b24c262171e7 +version: 2 +date: '2017-09-20' +description: This search queries the notable index and returns all the Notable Events + for the particular destination host, giving the analyst an overview of the incidents + that may have occurred with the host under investigation. +how_to_implement: If you are using Enterprise Security you are likely already creating + notable events with your correlation rules. No additional configuration is necessary. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| search `notable` | search dest=$dest$ | table _time, rule_name, owner, + priority, severity, status_description' +tags: + analytics_story: + - AWS Cross Account Activity + - AWS Cryptomining + - AWS Network ACL Activity + - AWS User Monitoring + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Asset Tracking + - Brand Monitoring + - Cloud Cryptomining + - ColdRoot MacOS RAT + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - DNS Amplification Attacks + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - JBoss Vulnerability + - Kubernetes Scanning Activity + - Lateral Movement + - Malicious PowerShell + - Monitor Backup Solution + - Monitor for Unauthorized Software + - Monitor for Updates + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SQL Injection + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Splunk Enterprise Vulnerability + - Splunk Enterprise Vulnerability CVE-2018-11409 + - Suspicious AWS EC2 Activities + - Suspicious AWS S3 Activities + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual AWS EC2 Modifications + - Unusual Processes + - Use of Cleartext Protocols + - Web Fraud Detection + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_notable_info.yml b/response_tasks/get_notable_info.yml new file mode 100644 index 0000000000..b51d7d48b7 --- /dev/null +++ b/response_tasks/get_notable_info.yml @@ -0,0 +1,69 @@ +name: Get Notable Info +id: f3fb4d1b-5f33-4b01-b541-c7af9534c242 +version: 1 +date: '2017-09-20' +description: This search queries the notable index to retrieve detailed information + captured within the notable. Every notable has a unique ID associated with it, which + is used to point us directly to the notable event under investigation. +how_to_implement: If you are using Enterprise Security you are likely already creating + notable events with your correlation rules. No additional configuration is necessary. +author: Bhavin Patel, Splunk +inputs: +- event_id +search: '| search `notable_by_id($event_id$)` | table time, rule_name, dest, dest_asset_id, + dest_owner, priority, severity, owner, status_description' +tags: + analytics_story: + - AWS Cryptomining + - AWS Network ACL Activity + - AWS User Monitoring + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Asset Tracking + - Brand Monitoring + - Cloud Cryptomining + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - DNS Amplification Attacks + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - JBoss Vulnerability + - Kubernetes Scanning Activity + - Lateral Movement + - Malicious PowerShell + - Monitor for Unauthorized Software + - Monitor for Updates + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SQL Injection + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Splunk Enterprise Vulnerability + - Splunk Enterprise Vulnerability CVE-2018-11409 + - Suspicious AWS EC2 Activities + - Suspicious AWS S3 Activities + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Use of Cleartext Protocols + - Web Fraud Detection + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml b/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml new file mode 100644 index 0000000000..42078e684d --- /dev/null +++ b/response_tasks/get_outbound_emails_to_hidden_cobra_threat_actors.yml @@ -0,0 +1,18 @@ +name: Get Outbound Emails to Hidden Cobra Threat Actors +id: 5df39b3f-347d-4869-b673-8r45ad4616fe +version: 1 +date: '2018-06-14' +description: 'This search returns the information of the users that sent emails to + the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, + and from `redhat@gmail.com`.' +how_to_implement: To successfully implement this search you must ingest your email + logs or capture unencrypted email communications within network traffic, and populate + the Email data model. +author: Bhavin Patel, Splunk +inputs: +- src_user +- recipient +search: '| from datamodel Email.All_Email | search recipient=misswang8107@gmail.com + OR src_user=redhat@gmail.com | stats count earliest(_time) as firstTime, latest(_time) + as lastTime values(dest) values(src) by src_user recipient | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)`' diff --git a/response_tasks/get_parent_process_info.yml b/response_tasks/get_parent_process_info.yml new file mode 100644 index 0000000000..6260214e89 --- /dev/null +++ b/response_tasks/get_parent_process_info.yml @@ -0,0 +1,49 @@ +name: Get Parent Process Info +id: fecf2918-670d-4f1c-872b-3d7317a41bf9 +version: 2 +date: '2019-02-28' +description: This search queries the Endpoint data model to give you details about + the parent process of a process running on a host which is under investigation. + Enter the values of the process name in question and the dest +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints to populate the Endpoint + data model in the Processes node. The command-line arguments are mapped to the "process" + field in the Endpoint data model. +author: Bhavin Patel, Splunk +inputs: +- process_name +- dest +search: '| tstats `summariesonly` count values(Processes.process) as process min(_time) + as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name + = $process_name$ Processes.dest = $dest$ by Processes.user Processes.parent_process_name Processes.process_name | + `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`' +tags: + analytics_story: + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - Disabling Security Tools + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Lateral Movement + - Malicious PowerShell + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Phishing Payloads + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - SamSam Ransomware + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_process_file_activity.yml b/response_tasks/get_process_file_activity.yml new file mode 100644 index 0000000000..541db0c677 --- /dev/null +++ b/response_tasks/get_process_file_activity.yml @@ -0,0 +1,20 @@ +name: Get Process File Activity +id: 6a9ad4d9-6ef2-4b85-953f-a37ab256acd5 +version: 2 +date: '2019-11-06' +description: This search returns the file activity for a specific process on a specific + endpoint +how_to_implement: To successfully implement this search you must be ingesting endpoint + data and populating the Endpoint data model. +author: David Dorsey, Splunk +inputs: +- process_id +- dest +search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as + file_name values(Filesystem.dest) as dest, values(Filesystem.process_id) as process_id + from datamodel=Endpoint.Filesystem where Filesystem.dest=$dest$ Filesystem.process_id=$process_id$ + by Filesystem.file_path, Filesystem.action, _time | `drop_dm_object_name(Filesystem)` | + sort _time | table _time, process_id, dest, action, file_name, file_path' +tags: + analytics_story: + - DHS Report TA18-074A diff --git a/response_tasks/get_process_info.yml b/response_tasks/get_process_info.yml new file mode 100644 index 0000000000..ae5ec4c163 --- /dev/null +++ b/response_tasks/get_process_info.yml @@ -0,0 +1,49 @@ +name: Get Process Info +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd71 +version: 2 +date: '2019-04-01' +description: This search queries the Endpoint data model to give you details about + the process running on a host which is under investigation. To gather the process + info, enter the values for the process name in question and the destination IP address. +how_to_implement: To successfully implement this search you must be ingesting endpoint + data and populating the Endpoint data model. +author: Bhavin Patel, Splunk +inputs: +- process_name +- dest +search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as + lastTime from datamodel=Endpoint.Processes where Proceses.dest=$dest$ Proceses.process_name=$process_name$ + by Processes.parent_process Processes.process_name Processes.user Processes.dest + | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` ' +tags: + analytics_story: + - AWS Network ACL Activity + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - Data Protection + - Disabling Security Tools + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Lateral Movement + - Malicious PowerShell + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - SamSam Ransomware + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_process_information_for_port_activity.yml b/response_tasks/get_process_information_for_port_activity.yml new file mode 100644 index 0000000000..f4973b5717 --- /dev/null +++ b/response_tasks/get_process_information_for_port_activity.yml @@ -0,0 +1,32 @@ +name: Get Process Information For Port Activity +id: 9925d08f-561e-4faa-8912-e3888a842341 +version: 2 +date: '2019-04-01' +description: This search will return information about the process associated with + observed network traffic to a specific destination port from a specific host. +how_to_implement: To successfully implement this search you must be ingesting endpoint + data that associates processes with network events and populate the Endpoint Datamodel +author: Bhavin Patel, Splunk +inputs: +- dest_port +- dest +search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as + lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.process_name + Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search + [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where + Ports.dest_port=$dest_port$ by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` + | rename src as dest]' +tags: + analytics_story: + - AWS Network ACL Activity + - Command and Control + - DHS Report TA18-074A + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Lateral Movement + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - SamSam Ransomware + - Suspicious AWS Traffic + - Use of Cleartext Protocols diff --git a/response_tasks/get_process_registry_activity.yml b/response_tasks/get_process_registry_activity.yml new file mode 100644 index 0000000000..71b74cb8ad --- /dev/null +++ b/response_tasks/get_process_registry_activity.yml @@ -0,0 +1,20 @@ +name: Get Process Registry Activity +id: d8362a34-b78a-4364-9733-59b505f5b8d5 +version: 2 +date: '2019-11-06' +description: This search returns the registry activity for a specific process on a + specific endpoint +how_to_implement: To successfully implement this search you must be ingesting endpoint + data and populating the Endpoint data model. +author: David Dorsey, Splunk +inputs: +- process_id +- dest +search: '| tstats `security_content_summariesonly` values(Registry.registry_key_name) + as registry_key_name, values(Registry.dest) as dest, values(Registry.process_id) + as process_id from datamodel=Endpoint.Registry where Registry.process_id=$process_id$ + AND Registry.dest=$dest$ by Registry.registry_path, Registry.action, _time | `drop_dm_object_name(Registry)` + | sort _time | table _time, process_id, dest, action, registry_key_name, registry_path' +tags: + analytics_story: + - DHS Report TA18-074A diff --git a/response_tasks/get_process_responsible_for_the_dns_traffic.yml b/response_tasks/get_process_responsible_for_the_dns_traffic.yml new file mode 100644 index 0000000000..2599737070 --- /dev/null +++ b/response_tasks/get_process_responsible_for_the_dns_traffic.yml @@ -0,0 +1,31 @@ +name: Get Process Responsible For The DNS Traffic +id: 910e6512-edc9-4f93-ba24-5b786f47a672 +version: 2 +date: '2019-04-01' +description: While investigating, an analyst will want to know what process and parent_process + is responsible for generating suspicious DNS traffic. Use the following search and + enter the value of `dest` in the search to get specific details on the process responsible + for creating the DNS traffic. +how_to_implement: You must be ingesting endpoint data that associates processes with + network events into the Endpoint datamodel. This can come from endpoint protection + products such as carbon black, or endpoint data sources such as Sysmon. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| tstats `security_content_summariesonly` count min(_time) max(_time) as + lastTime from datamodel=Endpoint.Processes where Processes.dest = $dest$ by Processes.parent_process + Processes.process_name Processes.user Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search + [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where + Ports.dest_port=53 by Ports.process_id Ports.src | `drop_dm_object_name(Ports)` + | rename src as dest]' +tags: + analytics_story: + - AWS Network ACL Activity + - Brand Monitoring + - Command and Control + - Data Protection + - Dynamic DNS + - Hidden Cobra Malware + - Suspicious AWS Traffic + - Suspicious DNS Traffic diff --git a/response_tasks/get_registry_activities.yml b/response_tasks/get_registry_activities.yml new file mode 100644 index 0000000000..5ee2c4385f --- /dev/null +++ b/response_tasks/get_registry_activities.yml @@ -0,0 +1,33 @@ +name: Get Registry Activities +id: fecf2918-670d-4f1c-872b-3d7317a41xf9 +version: 2 +date: '2019-03-01' +description: This search queries the Endpoint Datamodel to give you details of the + latest registry values for a specific destination computer. +how_to_implement: To successfully implement this search you need to be ingesting information + on registry changes that include the name of the process responsible for the changes + from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` + nodes. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| tstats `security_content_summariesonly` values(Registry.registry_path) + as registry_path values(Registry.registry_key_name) as registry_key_name count FROM + datamodel=Endpoint.Registry where Registry.dest = "$dest$" by Registry.process_id + Registry.dest | `drop_dm_object_name("Registry")` | join [| tstats `security_content_summariesonly` + count values(Processes.user) as user values(Processes.process_name) as process_name + values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes + where Processes.process_name = reg.exe by Processes.process_id | `drop_dm_object_name("Processes")`]' +tags: + analytics_story: + - DHS Report TA18-074A + - 'Emotet Malware DHS Report TA18-201A ' + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + - Suspicious Command-Line Executions + - Suspicious MSHTA Activity + - Suspicious Windows Registry Activities + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Persistence Techniques + - Windows Privilege Escalation diff --git a/response_tasks/get_risk_modifiers_for_endpoint.yml b/response_tasks/get_risk_modifiers_for_endpoint.yml new file mode 100644 index 0000000000..665c2dd28a --- /dev/null +++ b/response_tasks/get_risk_modifiers_for_endpoint.yml @@ -0,0 +1,65 @@ +name: Get Risk Modifiers For Endpoint +id: fdcfb369-1725-4c24-824a-22972d7f0d65 +version: 1 +date: '2017-10-19' +description: 'For the last 7 days, the search will query the Risk data model in Splunk + Enterprise Security and calculate the count, sum of the risk\_scores, names of the + correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) ' +how_to_implement: Enable the correlation searches included in Splunk Enterprise Security + that include Risk Analysis alert actions by leveraging the Risk Analysis Framework +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| from datamodel:Risk.All_Risk | search risk_object_type=system risk_object=$dest$ + | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime + max(_time) as lastTime by risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' +tags: + analytics_story: + - AWS Network ACL Activity + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Brand Monitoring + - ColdRoot MacOS RAT + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - DNS Amplification Attacks + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - JBoss Vulnerability + - Kubernetes Scanning Activity + - Lateral Movement + - Malicious PowerShell + - Monitor Backup Solution + - Monitor for Unauthorized Software + - Monitor for Updates + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SQL Injection + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Splunk Enterprise Vulnerability + - Splunk Enterprise Vulnerability CVE-2018-11409 + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Use of Cleartext Protocols + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_risk_modifiers_for_user.yml b/response_tasks/get_risk_modifiers_for_user.yml new file mode 100644 index 0000000000..37d53e5e5c --- /dev/null +++ b/response_tasks/get_risk_modifiers_for_user.yml @@ -0,0 +1,59 @@ +name: Get Risk Modifiers For User +id: fdcfb369-1725-4c24-824a-22972d7f0d55 +version: 1 +date: '2017-10-19' +description: 'For the last 7 days, the search will query the Risk data model in Splunk + Enterprise Security and calculate the count, sum of the risk_scores, names of the + correlation searches that contributed to create a risk score for a specific user ' +how_to_implement: Enable the correlation searches included in Splunk Enterprise Security + that include Risk Analysis alert actions by leveraging the Risk Analysis Framework +author: Bhavin Patel, Splunk +inputs: +- user +search: '| from datamodel:Risk.All_Risk | search risk_object_type=user risk_object=$user$ + | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime + max(_time) as lastTime by risk_object |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` ' +tags: + analytics_story: + - AWS Network ACL Activity + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Brand Monitoring + - ColdRoot MacOS RAT + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - DNS Amplification Attacks + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - Lateral Movement + - Malicious PowerShell + - Monitor Backup Solution + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Use of Cleartext Protocols + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_sysmon_wmi_activity_for_host.yml b/response_tasks/get_sysmon_wmi_activity_for_host.yml new file mode 100644 index 0000000000..eb6ba7909c --- /dev/null +++ b/response_tasks/get_sysmon_wmi_activity_for_host.yml @@ -0,0 +1,20 @@ +name: Get Sysmon WMI Activity for Host +id: 155e0571-7db6-42f2-aa62-9a3a4cf35c94 +version: 1 +date: '2018-10-23' +description: This search queries Sysmon WMI events for the host of interest. +how_to_implement: To successfully implement this search, you must be collecting Sysmon + data using Sysmon version 6.1 or greater and have Sysmon configured to generate + events for WMI activity. In addition, you must have at least version 6.0.4 of the + Sysmon TA installed to properly parse the fields. +author: Rico Valdez, Splunk +inputs: +- process +- dest +search: sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 + EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, + Operation, EventType, Type, Query, Consumer, Filter +tags: + analytics_story: + - Ransomware + - Suspicious WMI Use diff --git a/response_tasks/get_update_logs_for_endpoint.yml b/response_tasks/get_update_logs_for_endpoint.yml new file mode 100644 index 0000000000..0c26920660 --- /dev/null +++ b/response_tasks/get_update_logs_for_endpoint.yml @@ -0,0 +1,18 @@ +name: Get Update Logs For Endpoint +id: d98675ed-da43-4a7e-96a7-eeca3232ba8e +version: 1 +date: '2017-08-24' +description: This search will tell you give you the update logs for a specific endpoint + for the last week. +how_to_implement: You need to be ingesting the update logs from your various systems. +author: David Dorsey, Splunk +inputs: +- dest +search: '| from datamodel Updates.Updates | search (vendor_product="Microsoft Windows" + OR vendor_product="OSX:Update" OR vendor_product="Linux:Update") dest=$dest$' +tags: + analytics_story: + - 'Emotet Malware DHS Report TA18-201A ' + - Monitor for Unauthorized Software + - Ransomware + - SamSam Ransomware diff --git a/response_tasks/get_user_information_from_identity_table.yml b/response_tasks/get_user_information_from_identity_table.yml new file mode 100644 index 0000000000..60df58e6d6 --- /dev/null +++ b/response_tasks/get_user_information_from_identity_table.yml @@ -0,0 +1,58 @@ +name: Get User Information from Identity Table +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd74 +version: 1 +date: '2017-09-20' +description: Gather more information about the user identified in the Notable Event. +how_to_implement: To successfully implement this search you must have populated the + identity table with information about your users. +author: Bhavin Patel, Splunk +inputs: +- user +search: '| `identities` | search identity=$user$ | table _time, identity, first, last, + email, category, watchlist' +tags: + analytics_story: + - AWS Cryptomining + - AWS Network ACL Activity + - Account Monitoring and Controls + - Apache Struts Vulnerability + - Brand Monitoring + - Cloud Cryptomining + - ColdRoot MacOS RAT + - Collection and Staging + - Command and Control + - DHS Report TA18-074A + - Data Protection + - Disabling Security Tools + - Dynamic DNS + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - Host Redirection + - Lateral Movement + - Malicious PowerShell + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Prohibited Traffic Allowed or Protocol Mismatch + - Ransomware + - Router and Infrastructure Security + - SamSam Ransomware + - Spectre And Meltdown Vulnerabilities + - Suspicious AWS EC2 Activities + - Suspicious AWS S3 Activities + - Suspicious AWS Traffic + - Suspicious Command-Line Executions + - Suspicious DNS Traffic + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious WMI Use + - Suspicious Windows Registry Activities + - Unusual Processes + - Use of Cleartext Protocols + - Windows Defense Evasion Tactics + - Windows File Extension and Association Abuse + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation + - Windows Service Abuse diff --git a/response_tasks/get_vulnerability_logs_for_endpoint.yml b/response_tasks/get_vulnerability_logs_for_endpoint.yml new file mode 100644 index 0000000000..9daed1be94 --- /dev/null +++ b/response_tasks/get_vulnerability_logs_for_endpoint.yml @@ -0,0 +1,22 @@ +name: Get Vulnerability Logs For Endpoint +id: df7a7f50-30f2-4cde-8448-69d2d5f9b3c5 +version: 1 +date: '2017-09-10' +description: This search will show you any vulnerabilities noted for a specific endpoint + for the last week. +how_to_implement: You need to be ingesting the logs from your vulnerability scanner. +author: David Dorsey, Splunk +inputs: +- dest +search: '| from datamodel Vulnerabilities.Vulnerabilities | search dest=$dest$' +tags: + analytics_story: + - ColdRoot MacOS RAT + - DHS Report TA18-074A + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - JBoss Vulnerability + - Monitor for Unauthorized Software + - Ransomware + - SamSam Ransomware + - Windows Log Manipulation diff --git a/response_tasks/get_web_session_information_via_session_id.yml b/response_tasks/get_web_session_information_via_session_id.yml new file mode 100644 index 0000000000..d519ed6e79 --- /dev/null +++ b/response_tasks/get_web_session_information_via_session_id.yml @@ -0,0 +1,19 @@ +name: Get Web Session Information via session id +id: bc91a8cf-35e7-4bb2-1120-e756cc06fd89 +version: 1 +date: '2018-10-08' +description: This search helps an analyst investigate a notable event to find out + more about a specific web session. The search looks for a specific web session ID + in the HTTP web traffic and outputs the URL and user agents, grouped by source IP + address and HTTP status code. +how_to_implement: This search leverages data extracted from Stream:HTTP. You must + configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment + server. +author: Bhavin Patel, Splunk +inputs: +- session_id +search: '| search sourcetype=stream:http $session_id$ | stats values(url) values(http_user_agent) + by src_ip status' +tags: + analytics_story: + - Web Fraud Detection diff --git a/response_tasks/investigate_aws_activities_via_region_name.yml b/response_tasks/investigate_aws_activities_via_region_name.yml new file mode 100644 index 0000000000..ef214d8ea1 --- /dev/null +++ b/response_tasks/investigate_aws_activities_via_region_name.yml @@ -0,0 +1,21 @@ +name: Investigate AWS activities via region name +id: bc91a8cd-35e7-4bb2-6140-e756cc46fd11 +version: 1 +date: '2018-02-09' +description: This search lists all the user activities logged by CloudTrail for a + specific region in question and will create a table of the values of parameters + requested, the type of the event and the response from the AWS API by each user +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +inputs: +- awsRegion +search: '| search sourcetype=aws:cloudtrail awsRegion=$awsRegion$| rename requestParameters.instancesSet.items{}.instanceId + as instanceId| stats values(eventName) by userName instanceId' +tags: + analytics_story: + - AWS Cryptomining + - Cloud Cryptomining + - Suspicious AWS EC2 Activities + - Suspicious AWS S3 Activities diff --git a/response_tasks/investigate_aws_ecr_container_listing_activity.yml b/response_tasks/investigate_aws_ecr_container_listing_activity.yml new file mode 100644 index 0000000000..435c5c701a --- /dev/null +++ b/response_tasks/investigate_aws_ecr_container_listing_activity.yml @@ -0,0 +1,23 @@ +name: Investigate AWS ECR container listing activity +id: 6027623f-7d10-4847-af3b-8d7e87970451 +version: 1 +date: '2020-02-20' +description: This search lists all the users performing a list image operation on + AWS Elastic Container Registry. Listing source user, image id, source IP, user type, + http user agent. This search also gives counts of unique user agents per listing + source. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs.You must also install Cloud Infrastructure Data Model. +author: Rod Soto, Rico Valdez, Splunk +inputs: +- Compute.event_name +search: '|tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute + where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id + Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename + "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) + as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count + > 1' +tags: + analytics_story: + - Container Implantation Monitoring and Investigation diff --git a/response_tasks/investigate_aws_user_activities_by_user_field.yml b/response_tasks/investigate_aws_user_activities_by_user_field.yml new file mode 100644 index 0000000000..b2fcc388ae --- /dev/null +++ b/response_tasks/investigate_aws_user_activities_by_user_field.yml @@ -0,0 +1,19 @@ +name: Investigate AWS User Activities by user field +id: bc91a8cd-35e7-4bb2-6140-e756cc46fd76 +version: 1 +date: '2018-03-12' +description: This search lists all the logged CloudTrail activities by a specific + user and will create a table containing the source of the user, the region of the + activity, the name and type of the event, the action taken, and the user's identity + information. +how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) + and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail + inputs. +author: Bhavin Patel, Splunk +inputs: +- user +search: '| search sourcetype=aws:cloudtrail user=$user$ | table _time userIdentity.type + userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType ' +tags: + analytics_story: + - AWS User Monitoring diff --git a/response_tasks/investigate_cloud_compute_instance_activities.yml b/response_tasks/investigate_cloud_compute_instance_activities.yml new file mode 100644 index 0000000000..417a96ff09 --- /dev/null +++ b/response_tasks/investigate_cloud_compute_instance_activities.yml @@ -0,0 +1,15 @@ +name: Investigate Cloud Compute Instance Activities +id: 33a95cf2-900c-4636-8fca-5c5f71474720 +version: 1 +date: '2018-03-12' +description: This search returns a logs of events that operated on the compute instance. +how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs + and have the Security Research cloud data model installed. +author: David Dorsey, Splunk +inputs: +- dest +search: '| from datamodel:Cloud_Infrastructure.Compute | search dest=$dest$ | fields + - _*' +tags: + analytics_story: + - Cloud Cryptomining diff --git a/response_tasks/investigate_failed_logins_for_multiple_destinations.yml b/response_tasks/investigate_failed_logins_for_multiple_destinations.yml new file mode 100644 index 0000000000..fd0ffd8e6c --- /dev/null +++ b/response_tasks/investigate_failed_logins_for_multiple_destinations.yml @@ -0,0 +1,19 @@ +name: Investigate Failed Logins for Multiple Destinations +id: 097e8030-8662-4254-a735-bf0bdda696e3 +version: 1 +date: '2019-12-10' +description: This search returns failed logins to multiple destinations by user. +how_to_implement: To successfully implement this search you need to be ingesting authentication + logs from your various systems and populating the Authentication data model. +author: Patrick Bareiss, Splunk +inputs: +- dest +search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login + latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) + AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication + where Authentication.action=failure by Authentication.user | where distinct_count_dest + > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` + | `drop_dm_object_name("Authentication")`' +tags: + analytics_story: + - Credential Dumping diff --git a/response_tasks/investigate_network_traffic_from_src_ip.yml b/response_tasks/investigate_network_traffic_from_src_ip.yml new file mode 100644 index 0000000000..af381cae68 --- /dev/null +++ b/response_tasks/investigate_network_traffic_from_src_ip.yml @@ -0,0 +1,16 @@ +name: Investigate Network Traffic From src ip +id: 9df9ca9c-a02b-4f48-9eba-0bac55179050 +version: 1 +date: '2018-06-15' +description: This search allows you to find all the network traffic from a specific + IP address. +how_to_implement: To successfully implement this search, you must be ingesting your + web-traffic logs and populating the web data model. +author: David Dorsey, Splunk +inputs: +- src_ip +search: '| from datamodel Network_Traffic.All_Traffic | search src_ip=$src_ip$' +tags: + analytics_story: + - ColdRoot MacOS RAT + - Splunk Enterprise Vulnerability CVE-2018-11409 diff --git a/response_tasks/investigate_okta_activity_by_app.yml b/response_tasks/investigate_okta_activity_by_app.yml new file mode 100644 index 0000000000..b281e92d21 --- /dev/null +++ b/response_tasks/investigate_okta_activity_by_app.yml @@ -0,0 +1,15 @@ +name: Investigate Okta Activity by app +id: 420eb1b8-2992-45d1-80cf-0b1b2759524d +version: 1 +date: '2020-04-02' +description: This search returns all okta events associated with a specific app +how_to_implement: You must be ingesting Okta logs +author: Rico Valdez, Splunk +inputs: +- app +search: eventtype=okta_log app=$app$ | rename client.geographicalContext.country as + country, client.geographicalContext.state as state, client.geographicalContext.city + as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason +tags: + analytics_story: + - Suspicious Okta Activity diff --git a/response_tasks/investigate_okta_activity_by_ip_address.yml b/response_tasks/investigate_okta_activity_by_ip_address.yml new file mode 100644 index 0000000000..4216491b2a --- /dev/null +++ b/response_tasks/investigate_okta_activity_by_ip_address.yml @@ -0,0 +1,15 @@ +name: Investigate Okta Activity by IP Address +id: 56aae066-d619-477c-93e3-3fb83b2d23c3 +version: 1 +date: '2020-04-02' +description: This search returns all okta events from a specific IP address. +how_to_implement: You must be ingesting Okta logs +author: Rico Valdez, Splunk +inputs: +- user +search: eventtype=okta_log src_ip={src_ip} | rename client.geographicalContext.country + as country, client.geographicalContext.state as state, client.geographicalContext.city + as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason +tags: + analytics_story: + - Suspicious Okta Activity diff --git a/response_tasks/investigate_pass_the_hash_attempts.yml b/response_tasks/investigate_pass_the_hash_attempts.yml new file mode 100644 index 0000000000..cba30c704f --- /dev/null +++ b/response_tasks/investigate_pass_the_hash_attempts.yml @@ -0,0 +1,20 @@ +name: Investigate Pass the Hash Attempts +id: ed3fff45-cba6-4990-983f-6fac72bee659 +version: 1 +date: '2019-12-10' +description: This search hunts for dumped NTLM hashes used for pass the hash. +how_to_implement: To successfully implement this search you need be ingesting windows + security logs. This search uses an input macro named `wineventlog_security`. We + strongly recommend that you specify your environment-specific configurations (index, + source, sourcetype, etc.) for Windows Security logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +author: Patrick Bareiss, Splunk +inputs: +- dest +search: '`wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate + | stats count earliest(_time) as first_login latest(_time) as last_login by src_user + dest | `security_content_ctime(first_login)` | `security_content_ctime(last_login)`' +tags: + analytics_story: + - Credential Dumping diff --git a/response_tasks/investigate_pass_the_ticket_attempts.yml b/response_tasks/investigate_pass_the_ticket_attempts.yml new file mode 100644 index 0000000000..7ca5f470c9 --- /dev/null +++ b/response_tasks/investigate_pass_the_ticket_attempts.yml @@ -0,0 +1,21 @@ +name: Investigate Pass the Ticket Attempts +id: 990007ad-d798-4b29-ab2f-f0034144c937 +version: 1 +date: '2019-12-10' +description: This search hunts for dumped kerberos ticket from LSASS memory. +how_to_implement: To successfully implement this search you need to be ingesting windows + security logs. This search uses an input macro named `wineventlog_security`. We + strongly recommend that you specify your environment-specific configurations (index, + source, sourcetype, etc.) for Windows Security logs. Replace the macro definition + with configurations for your Splunk environment. The search also uses a post-filter + macro designed to filter out known false positives. +author: Patrick Bareiss, Splunk +inputs: +- dest +search: '`wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user + "(?[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) + AS max_count sum(count) AS sum_count BY new_user, dest | where sum_count/max_count!=2 + | rename new_user AS user' +tags: + analytics_story: + - Credential Dumping diff --git a/response_tasks/investigate_previous_unseen_user.yml b/response_tasks/investigate_previous_unseen_user.yml new file mode 100644 index 0000000000..56ac2e87e5 --- /dev/null +++ b/response_tasks/investigate_previous_unseen_user.yml @@ -0,0 +1,22 @@ +name: Investigate Previous Unseen User +id: 5de385bf-4f1e-404e-9b67-92d162ff8938ad +version: 1 +date: '2019-12-10' +description: This search returns previous unseen user, which didn't log in for 30 + days. +how_to_implement: To successfully implement this search you need to be ingesting authentication + logs from your various systems and populating the Authentication data model. +author: Patrick Bareiss, Splunk +inputs: +- dest +search: '| tstats count `security_content_summariesonly` earliest(_time) as first_login + latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) + AS Authentication.app values(Authentication.action) AS Authentication.action from + datamodel=Authentication where Authentication.action=success by _time, Authentication.user + | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) + as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user + | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` + | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")`' +tags: + analytics_story: + - Credential Dumping diff --git a/response_tasks/investigate_successful_remote_desktop_authentications.yml b/response_tasks/investigate_successful_remote_desktop_authentications.yml new file mode 100644 index 0000000000..41f4407bec --- /dev/null +++ b/response_tasks/investigate_successful_remote_desktop_authentications.yml @@ -0,0 +1,23 @@ +name: Investigate Successful Remote Desktop Authentications +id: b6618e8e-be04-40a0-a0b9-f0bd4b6c81bc +version: 1 +date: '2018-12-14' +description: 'This search returns the source, destination, and user for all successful + remote-desktop authentications. A successful authentication after a brute-force + attack on a destination machine is suspicious behavior. ' +how_to_implement: You must be populating the Authentication data model with security + events from your Windows event logs. +author: Jose Hernandez, Splunk +inputs: +- dest +search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) + as lastTime from datamodel=Authentication where Authentication.signature_id=4624 + Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app + Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` + | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")`| + table firstTime lastTime src src_nt_domain dest user app count | sort count' +tags: + analytics_story: + - Hidden Cobra Malware + - Lateral Movement + - SamSam Ransomware diff --git a/response_tasks/investigate_suspicious_strings_in_http_header.yml b/response_tasks/investigate_suspicious_strings_in_http_header.yml new file mode 100644 index 0000000000..9bc50b2b36 --- /dev/null +++ b/response_tasks/investigate_suspicious_strings_in_http_header.yml @@ -0,0 +1,26 @@ +name: Investigate Suspicious Strings in HTTP Header +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd89 +version: 1 +date: '2017-10-20' +description: This search helps an analyst investigate a notable event related to a + potential Apache Struts exploitation. To investigate, we will want to isolate and + analyze the "payload" or the commands that were passed to the vulnerable hosts by + creating a few regular expressions to carve out the commands focusing on common + keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns + these suspicious strings found in the HTTP logs of the system of interest. +how_to_implement: This particular search leverages data extracted from Stream:HTTP. + You must configure the http stream using the Splunk Stream App on your Splunk Stream + deployment server to extract the cs_content_type field. +author: Bhavin Patel, Splunk +inputs: +- src_ip +- dest_ip +search: '| search sourcetype=stream:http src_ip="$src_ip$" dest_ip="$dest_ip$" | eval + cs_content_type_length = len(cs_content_type) | search cs_content_type_length > + 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, + "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious + Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, + cs_content_type, cs_content_type_length, url' +tags: + analytics_story: + - Apache Struts Vulnerability diff --git a/response_tasks/investigate_user_activities_in_all_cloud_regions.yml b/response_tasks/investigate_user_activities_in_all_cloud_regions.yml new file mode 100644 index 0000000000..e162510d2a --- /dev/null +++ b/response_tasks/investigate_user_activities_in_all_cloud_regions.yml @@ -0,0 +1,17 @@ +name: Investigate User Activities In All Cloud Regions +id: 2ef6310f-8e79-42af-b20b-b4eeaba9608a +version: 2 +date: '2019-04-30' +description: This search lists all the logged cloud infrastructure activities by a + specific cloud user +how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs + and have the Security Research cloud data model installed. +author: David Dorsey, Splunk +inputs: +- region +- src_user +search: '| from datamodel:Cloud_Infrastructure.Compute | search user=$src_user$ | + fields - _*' +tags: + analytics_story: + - Cloud Cryptomining diff --git a/response_tasks/investigate_user_activities_in_okta.yml b/response_tasks/investigate_user_activities_in_okta.yml new file mode 100644 index 0000000000..942d9d7e76 --- /dev/null +++ b/response_tasks/investigate_user_activities_in_okta.yml @@ -0,0 +1,15 @@ +name: Investigate User Activities In Okta +id: 24ff145d-4d16-420a-b047-480f2a51c403 +version: 1 +date: '2020-04-02' +description: This search returns all okta events by a specific user +how_to_implement: You must be ingesting Okta logs +author: Rico Valdez, Splunk +inputs: +- user +search: eventtype=okta_log user=$user$ | rename client.geographicalContext.country + as country, client.geographicalContext.state as state, client.geographicalContext.city + as city | table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason +tags: + analytics_story: + - Suspicious Okta Activity diff --git a/response_tasks/investigate_user_activities_in_single_cloud_region.yml b/response_tasks/investigate_user_activities_in_single_cloud_region.yml new file mode 100644 index 0000000000..be63a4df81 --- /dev/null +++ b/response_tasks/investigate_user_activities_in_single_cloud_region.yml @@ -0,0 +1,17 @@ +name: Investigate User Activities In Single Cloud Region +id: 3dc3a8e7-394b-44ae-8262-4ef8e90b723d +version: 2 +date: '2019-04-30' +description: This search lists all the logged cloud infrastructure activities by a + specific cloud user in a specific cloud region +how_to_implement: You must be ingesting the approrpiate cloud infrastructure logs + and have the Security Research cloud data model installed. +author: David Dorsey, Splunk +inputs: +- region +- src_user +search: '| from datamodel:Cloud_Infrastructure.Compute | search region=$region$ user=$src_user$ + | fields - _*' +tags: + analytics_story: + - Cloud Cryptomining diff --git a/response_tasks/investigate_web_activity_from_host.yml b/response_tasks/investigate_web_activity_from_host.yml new file mode 100644 index 0000000000..e8b14e65f3 --- /dev/null +++ b/response_tasks/investigate_web_activity_from_host.yml @@ -0,0 +1,35 @@ +name: Investigate Web Activity From Host +id: bc91a8cf-35e7-4bb2-8140-e756cc06fd22 +version: 1 +date: '2017-11-09' +description: This search allows you to find all the web activity from a specific host. + During an investigation, it is important to profile web activity to characterize + user or host activity. +how_to_implement: To successfully implement this search you must be ingesting your + web traffic and populating the Web data model. +author: Bhavin Patel, Splunk +inputs: +- dest +search: '| from datamodel Web.Web | search src=$dest$' +tags: + analytics_story: + - Brand Monitoring + - DHS Report TA18-074A + - Disabling Security Tools + - 'Emotet Malware DHS Report TA18-201A ' + - Hidden Cobra Malware + - JBoss Vulnerability + - Monitor for Unauthorized Software + - Netsh Abuse + - Orangeworm Attack Group + - Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + - Ransomware + - SamSam Ransomware + - Suspicious Command-Line Executions + - Suspicious Emails + - Suspicious MSHTA Activity + - Suspicious Windows Registry Activities + - Unusual Processes + - Windows Log Manipulation + - Windows Persistence Techniques + - Windows Privilege Escalation diff --git a/response_tasks/investigate_web_activity_from_src_ip.yml b/response_tasks/investigate_web_activity_from_src_ip.yml new file mode 100644 index 0000000000..f23344f44a --- /dev/null +++ b/response_tasks/investigate_web_activity_from_src_ip.yml @@ -0,0 +1,18 @@ +name: Investigate Web Activity From src ip +id: 2f5b960b-71df-49c0-affc-74992ce60e45 +version: 1 +date: '2018-06-15' +description: This search searches for all web activity from a specific host. During + an investigation, it is important to profile web activity to characterize user or + host activity. +how_to_implement: To successfully implement this search, you must be ingesting your + web traffic and populating the web data model. +author: David Dorsey, Splunk +inputs: +- src_ip +search: '| from datamodel Web.Web | search src=$src_ip$' +tags: + analytics_story: + - ColdRoot MacOS RAT + - Dynamic DNS + - Splunk Enterprise Vulnerability CVE-2018-11409 diff --git a/response_tasks/investigate_web_posts_from_src.yml b/response_tasks/investigate_web_posts_from_src.yml new file mode 100644 index 0000000000..820de7a950 --- /dev/null +++ b/response_tasks/investigate_web_posts_from_src.yml @@ -0,0 +1,18 @@ +name: Investigate Web POSTs From src +id: f5c39fac-205c-4e07-9004-8fd61ea3431a +version: 1 +date: '2018-12-06' +description: 'This investigative search retrieves POST requests from a specified source + IP or hostname. Identifying the POST requests, as well as their associated destination + URLs and user agent(s), may help you scope and characterize the suspicious traffic. ' +how_to_implement: To successfully implement this search, you must be ingesting your + web-traffic logs and populating the web data model. +author: Jose Hernandez, Splunk +inputs: +- src +search: '| tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web + by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where + like(src, "$src$") and like(http_method, "POST")' +tags: + analytics_story: + - Apache Struts Vulnerability diff --git a/response_tasks/malware_hunt_and_contain.yml b/response_tasks/malware_hunt_and_contain.yml new file mode 100644 index 0000000000..b16e82a2a1 --- /dev/null +++ b/response_tasks/malware_hunt_and_contain.yml @@ -0,0 +1,10 @@ +name: Malware Hunt and Contain +id: 1d7b437a-5114-4b94-a585-04c3362ba08f +version: 1 +date: '2020-04-29' +description: Use the previous identified findings and hunt for the indicators of the malicious file. +author: Patrick Bareiss, Splunk +playbook: + name: malware_hunt_and_contain + url_json: https://github.com/phantomcyber/playbooks/blob/4.6/malware_hunt_and_contain.json + url_python: https://github.com/phantomcyber/playbooks/blob/4.6/malware_hunt_and_contain.py diff --git a/response_tasks/process_chain_analysis.yml b/response_tasks/process_chain_analysis.yml new file mode 100644 index 0000000000..2d4c77f9fd --- /dev/null +++ b/response_tasks/process_chain_analysis.yml @@ -0,0 +1,9 @@ +name: Process Chain Analysis +id: c5506139-ef86-4cd9-8535-0512aa732e79 +version: 1 +date: '2020-04-29' +description: Analyze the Process Chain and identify the malicious file. By analyzing the parent process guid and searching for the process guid, the spawning process chain can be identified. +author: Patrick Bareiss, Splunk +inputs: +- process_guid +search: '`sysmon` EventCode=1 NOT process=*Splunk* | rename process_guid AS out_process_guid process_name AS out_process_name parent_process_guid AS out_parent_process_guid parent_process_name AS out_parent_process_name | stats count by out_process_guid out_process_name out_parent_process_guid out_parent_process_name | eval join_process_guid = out_process_guid | join join_process_guid [ search `sysmon` process_guid={process_guid} EventCode=1 | rename process_name AS sub_process_name process_guid AS sub_process_guid parent_process_name AS sub_parent_process_name parent_process_guid AS sub_parent_process_guid | stats count by sub_process_name sub_process_guid sub_parent_process_name sub_parent_process_guid | eval join_process_guid = sub_parent_process_guid] | rename sub_process_guid AS process_guid sub_process_name AS process_name out_process_guid AS parent_process_guid out_process_name AS parent_process_name out_parent_process_guid AS grandparent_process_guid out_parent_process_name AS grandparent_process_name | stats count by process_guid process_name parent_process_guid parent_process_name grandparent_process_guid grandparent_process_name | head 1 | fields - count' diff --git a/response_tasks/quarantaine_infected_host.yml b/response_tasks/quarantaine_infected_host.yml new file mode 100644 index 0000000000..6c004211b8 --- /dev/null +++ b/response_tasks/quarantaine_infected_host.yml @@ -0,0 +1,6 @@ +name: Quarantaine Infected Host +id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78 +version: 1 +date: '2020-04-29' +description: Quarantine the infected hosts in order to stop the malware from spreading to further hosts. This is a short-term containment with the focus on limiting the damage as soon as possible. +author: Patrick Bareiss, Splunk diff --git a/response_tasks/suspicious_email_attachment_investigate_and_delete.yml b/response_tasks/suspicious_email_attachment_investigate_and_delete.yml new file mode 100644 index 0000000000..d786d34ee0 --- /dev/null +++ b/response_tasks/suspicious_email_attachment_investigate_and_delete.yml @@ -0,0 +1,17 @@ +name: Suspicious Email Attachment Investigate and Delete +id: 3096f721-8842-42ce-2fc7-742d8372b712 +version: 1 +date: '2019-06-03' +description: Investigate an email with a suspicious file attachment detected by Splunk + Enterprise Security. Detonate the file attachment in a sandbox, gather network behavior + from the sandbox results, and pivot on those network indicators with both external + reputation queries and internal Splunk Common Information Model searches. After + confirming the results with an analyst prompt, delete the email from the user's + inbox, hopefully before they have opened it. +how_to_implement: Synchronize the community playbook repository in Phantom, then open + the playbook and follow the deployment notes to configure it for your environment. +author: Philip Royer, Splunk +playbook: + name: suspicious_email_attachment_investigate_and_delete + url_json: https://github.com/phantomcyber/playbooks/blob/4.6/suspicious_email_attachment_investigate_and_delete.json + url_python: https://github.com/phantomcyber/playbooks/blob/4.6/suspicious_email_attachment_investigate_and_delete.py diff --git a/responses/README.md b/responses/README.md deleted file mode 100644 index e36fb6a2a5..0000000000 --- a/responses/README.md +++ /dev/null @@ -1,2 +0,0 @@ -### Comming soon to a theater near you - diff --git a/responses/credential_dumping_attack.yml b/responses/credential_dumping_attack.yml new file mode 100644 index 0000000000..875341c547 --- /dev/null +++ b/responses/credential_dumping_attack.yml @@ -0,0 +1,19 @@ +name: Credential Dumping Attack +id: 570dd98e-6cab-443c-bdd8-3dbb5fe4188d +version: 1 +date: '2020-04-29' +description: This response workflow guide you through the investigation of a credential dumping attack. +author: Patrick Bareiss, Splunk +response_tasks: + - identification: + - id: c5506139-ef86-4cd9-8535-0512aa732e79 + name: Process Chain Analysis + - id: 6ee5c067-8228-4926-abb2-54f2c59d726e + name: Analyze Malicious File + - id: 1d7b437a-5114-4b94-a585-04c3362ba08f + name: Malware Hunt and Contain + - containment: + - id: 60c4cfa5-81b7-44e2-9ad4-71524e4a3e78 + name: Quarantaine Infected Host +tags: + analytics_story: Credential Dumping diff --git a/spec/baselines.spec.json b/spec/baselines.spec.json new file mode 100644 index 0000000000..c13fc47497 --- /dev/null +++ b/spec/baselines.spec.json @@ -0,0 +1,107 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "description": "schema for baselines", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the baseline", + "examples": [ + "Bahvin Patel, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "A detailed description of the baseline ", + "examples": [ + "This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days" + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fc0edc95-ff2b-48b0-9f6f-63da3789fd63" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "examples": [ + "Previously Seen AWS Regions" + ], + "title": "Name of baseline", + "type": "string" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "The Splunk search for the baseline", + "examples": [ + "cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "suspicious_aws_ec2_activities", + "custom_key": "custom_value" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of baseline, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "search", + "tags" + ], + "title": "Baseline Schema", + "type": "object" +} diff --git a/spec/deployments.spec.json b/spec/deployments.spec.json new file mode 100644 index 0000000000..c417cc0bf3 --- /dev/null +++ b/spec/deployments.spec.json @@ -0,0 +1,264 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for deployment", + "properties": { + "alert_action": { + "$id": "#/properties/alert_action", + "additionalProperties": true, + "default": {}, + "description": "Set alert action parameter for search", + "examples": [ + { + "email": { + "message": "Splunk Alert $name$ triggered %fields%", + "subject": "Splunk Alert $name$", + "to": "test@test.com" + }, + "index": { + "name": "asx" + }, + "notable": { + "rule_description": "%description%", + "rule_title": "%name%" + } + } + ], + "properties": { + "email": { + "$id": "#/properties/alert_action/properties/email", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, an email is sent with the results", + "examples": [ + { + "message": "Splunk Alert $name$ triggered %fields%", + "subject": "Splunk Alert $name$", + "to": "test@test.com" + } + ], + "properties": { + "message": { + "$id": "#/properties/alert_action/properties/email/properties/message", + "default": "", + "description": "message of email", + "examples": [ + "Splunk Alert $name$ triggered %fields%" + ], + "type": "string" + }, + "subject": { + "$id": "#/properties/alert_action/properties/email/properties/subject", + "default": "", + "description": "Subject of email", + "examples": [ + "Splunk Alert $name$" + ], + "type": "string" + }, + "to": { + "$id": "#/properties/alert_action/properties/email/properties/to", + "default": "", + "description": "Recipient of email", + "examples": [ + "test@test.com" + ], + "type": "string" + } + }, + "required": [ + "to", + "subject", + "message" + ], + "type": "object" + }, + "index": { + "$id": "#/properties/alert_action/properties/index", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, the results are stored in another index", + "examples": [ + { + "name": "asx" + } + ], + "properties": { + "name": { + "$id": "#/properties/alert_action/properties/index/properties/name", + "default": "", + "description": "Name of the index", + "examples": [ + "asx" + ], + "type": "string" + } + }, + "required": [ + "name" + ], + "type": "object" + }, + "notable": { + "$id": "#/properties/alert_action/properties/notable", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, a notable is generated", + "examples": [ + { + "rule_description": "%description%", + "rule_title": "%name%" + } + ], + "properties": { + "rule_description": { + "$id": "#/properties/alert_action/properties/notable/properties/rule_description", + "default": "", + "description": "Rule description of the notable event", + "examples": [ + "%description%" + ], + "type": "string" + }, + "rule_title": { + "$id": "#/properties/alert_action/properties/notable/properties/rule_title", + "default": "", + "description": "Rule title of the notable event", + "examples": [ + "%name%" + ], + "type": "string" + } + }, + "required": [ + "rule_title", + "rule_description" + ], + "type": "object" + } + }, + "type": "object" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "description of the deployment configuration", + "examples": [ + "This deployment configuration provides a standard scheduling policy over all rules." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "uuid as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of deployment configuration", + "examples": [ + "Deployment Configuration all Detections" + ], + "type": "string" + }, + "scheduling": { + "$id": "#/properties/scheduling", + "additionalProperties": true, + "default": {}, + "description": "allows to set scheduling parameter", + "examples": [ + { + "cron_schedule": "*/10 * * * *", + "earliest_time": "-10m", + "latest_time": "now", + "schedule_window": "auto" + } + ], + "properties": { + "cron_schedule": { + "$id": "#/properties/scheduling/properties/cron_schedule", + "default": "", + "description": "Cron schedule to schedule the Splunk searches.", + "examples": [ + "*/10 * * * *" + ], + "type": "string" + }, + "earliest_time": { + "$id": "#/properties/scheduling/properties/earliest_time", + "default": "", + "description": "earliest time of search", + "examples": [ + "-10m" + ], + "type": "string" + }, + "latest_time": { + "$id": "#/properties/scheduling/properties/latest_time", + "default": "", + "description": "latest time of search", + "examples": [ + "now" + ], + "type": "string" + }, + "schedule_window": { + "$id": "#/properties/scheduling/properties/schedule_window", + "default": "", + "description": "schedule window for search", + "examples": [ + "auto" + ], + "type": "string" + } + }, + "required": [ + "cron_schedule", + "earliest_time", + "latest_time" + ], + "type": "object" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + } + }, + "required": [ + "name", + "id", + "date", + "description", + "scheduling", + "alert_action", + "tags" + ], + "title": "Deployment Schema", + "type": "object" +} diff --git a/spec/detections.spec.json b/spec/detections.spec.json new file mode 100644 index 0000000000..9f9fc25482 --- /dev/null +++ b/spec/detections.spec.json @@ -0,0 +1,157 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "description": "schema for detections", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the detection", + "examples": [ + "Patrick Bareiss, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "A detailed description of the detection", + "examples": [ + "dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump." + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "known_false_positives": { + "$id": "#/properties/knwon_false_positives", + "default": "", + "description": "known false postives", + "examples": [ + "Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual." + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "examples": [ + "Access LSASS Memory for Dump Creation" + ], + "title": "Name of detection", + "type": "string" + }, + "references": { + "$id": "#/properties/references", + "additionalItems": true, + "default": [], + "description": "A list of references for this detection", + "examples": [ + [ + "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf" + ] + ], + "items": { + "$id": "#/properties/references/items", + "default": "", + "description": "An explanation about the purpose of this instance.", + "examples": [ + "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf" + ], + "title": "The Items Schema", + "type": "string" + }, + "type": "array" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "The Splunk search for the detection", + "examples": [ + "`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping", + "custom_key": "custom_value" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "type": { + "$id": "#/properties/type", + "default": "", + "description": "type of detection", + "examples": [ + "ESCU" + ], + "items": { + "enum": [ + "ESCU", + "SSE", + "RBA" + ], + "type": "string" + }, + "type": "string" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 2 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "type", + "author", + "search", + "known_false_positives", + "tags" + ], + "title": "Detection Schema", + "type": "object" +} diff --git a/spec/v2/lookups.spec.json b/spec/lookups.spec.json similarity index 100% rename from spec/v2/lookups.spec.json rename to spec/lookups.spec.json diff --git a/spec/macros.spec.json b/spec/macros.spec.json new file mode 100644 index 0000000000..cd0f5cafe1 --- /dev/null +++ b/spec/macros.spec.json @@ -0,0 +1,43 @@ +{ + "$id": "https://api.splunkresearch.com/schemas/macros.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "description": "An object that defines the parameters for a Splunk Macro", + "properties": { + "arguments": { + "description": "A list of the arguments being passed to this macro", + "items": { + "type": "string" + }, + "minItems": 0, + "type": "array", + "uniqueItems": true + }, + "definition": { + "description": "The macro definition", + "examples": [ + "(query=fls-na* AND query = www* AND query=images*)" + ], + "type": "string" + }, + "description": { + "description": "What the macro is intended to filter", + "examples": [ + "Use this macro to filter out known good objects" + ], + "type": "string" + }, + "name": { + "description": "The name of the macro", + "examples": [ + "detection_search_output_filter" + ], + "type": "string" + } + }, + "required": [ + "name", + "description" + ], + "title": "Macro Manifest", + "type": "object" +} diff --git a/spec/response_tasks.spec.json b/spec/response_tasks.spec.json new file mode 100644 index 0000000000..257e12f5fb --- /dev/null +++ b/spec/response_tasks.spec.json @@ -0,0 +1,159 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for response tasks", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of response task", + "examples": [ + "Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "dashboard": { + "$id": "#/properties/dashboard", + "default": "", + "description": "Name of dashboard used as response task", + "examples": [ + "process_chain_analysis.json" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "Description of response task", + "examples": [ + "Response Task example description" + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "inputs": { + "$id": "#/properties/inputs", + "default": [], + "description": "Inputs used from the response task", + "examples": [ + [ + "lookup_file" + ] + ], + "type": "array" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Namo fo response task", + "examples": [ + "Response Tas Example" + ], + "type": "string" + }, + "playbook": { + "$id": "#/properties/playbook", + "additionalProperties": true, + "default": {}, + "description": "A phantom playbook as response task", + "examples": [ + { + "name": "lets_encrypt_domain_investigate.json", + "url_json": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json", + "url_python": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py" + } + ], + "properties": { + "name": { + "$id": "#/properties/playbook/properties/name", + "default": "", + "description": "Name of Phantom Playbook", + "examples": [ + "lets_encrypt_domain_investigate.json" + ], + "type": "string" + }, + "url_json": { + "$id": "#/properties/playbook/properties/url_json", + "default": "", + "description": "URL for phantom playbook json file", + "examples": [ + "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json" + ], + "type": "string" + }, + "url_python": { + "$id": "#/properties/playbook/properties/url_python", + "default": "", + "description": "URL for phantom playbook python file", + "examples": [ + "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py" + ], + "type": "string" + } + }, + "required": [ + "name", + "url_json", + "url_python" + ], + "type": "object" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "Search as response task", + "examples": [ + "`sysmon` EventCode=1 | search [| inputlookup %lookup_file% ] | stats count by dest user process_name" + ], + "type": "string" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 3 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author" + ], + "title": "Response Task Schema", + "type": "object" +} diff --git a/spec/responses.spec.json b/spec/responses.spec.json new file mode 100644 index 0000000000..f14d63f109 --- /dev/null +++ b/spec/responses.spec.json @@ -0,0 +1,117 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for response", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the response", + "examples": [ + "Rico Valdez, Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "Description of response", + "examples": [ + "Response example." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of response", + "examples": [ + "Response Example" + ], + "type": "string" + }, + "response_tasks": { + "$id": "#/properties/response_tasks", + "additionalProperties": true, + "default": {}, + "description": "Response tasks divided into phases", + "examples": [ + { + "another_phase": [ + { + "id": "7c72d944-3995-4485-8e57-67b4c353989b", + "name": "Another investigation" + } + ], + "identification": [ + { + "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892", + "name": "Investigate Indicator of Compromise Hash" + }, + { + "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0", + "name": "Investigate Domains" + } + ] + } + ], + "minItems": 1, + "type": "array" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "response_tasks", + "tags" + ], + "title": "Response Schema", + "type": "object" +} diff --git a/spec/stories.spec.json b/spec/stories.spec.json new file mode 100644 index 0000000000..3eda58b394 --- /dev/null +++ b/spec/stories.spec.json @@ -0,0 +1,106 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema analytics story", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the analytics story", + "examples": [ + "Rico Valdez, Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "description of the analytics story", + "examples": [ + "Uncover activity consistent with credential dumping, a technique where attackers compromise systems and attempt to obtain and exfiltrate passwords." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of the Analytics Story", + "examples": [ + "Credential Dumping" + ], + "type": "string" + }, + "narrative": { + "$id": "#/properties/narrative", + "default": "", + "description": "narrative of the analytics story", + "examples": [ + "gathering credentials from a target system, often hashed or encrypted, is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems." + ], + "type": "string" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "An additional Splunk search, which uses the result of the detections", + "examples": [ + "index=asx mitre_id=t1003 | stats values(source) as detections values(process) as processes values(user) as users values(_time) as time count by dest" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An explanation about the purpose of this instance.", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of analytics story, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "narrative", + "author", + "tags" + ], + "title": "Analytics Story Schema", + "type": "object" +} diff --git a/spec/v1/analytic_story.json.spec b/spec/v1/analytic_story.json.spec deleted file mode 100644 index aff21a1eb0..0000000000 --- a/spec/v1/analytic_story.json.spec +++ /dev/null @@ -1,163 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-04/schema#", - "title": "Analytic Story Manifest", - "description": "The fields that make up the manifest of a version 1 Analytic Story", - "type": "object", - "properties": { - "category": { - "description": "The category to which the Analytic Story belongs", - "enum": ["Abuse", "Adversary Tactics", "Best Practices", "Cloud Security", "Malware", "Vulnerability"] - }, - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Content Updates are in the ESCU channel", - "type": "string" - }, - "creation_date": { - "description": "The date this story was created", - "type": "string" - }, - "description": { - "description": "A high-level description or goal of the Analytic Story", - "type": "string" - }, - "id": { - "description": "A unique identifier for the Analytic Story", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person maintaining this Analytic Story" - }, - "email": { - "type": "string", - "description": "Email address of the person maintaining this Analytic Story" - }, - "name": { - "type": "string", - "description": "Name of the person maintaining this Analytic Story" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "modification_date": { - "description": "The date of the most recent modification to this Analytic Story", - "type": "string" - }, - "name": { - "description": "The name of the Analytic Story", - "type": "string" - }, - "narrative": { - "description": "Long-form text that describes the Analytic Story and the rationale behind it, as well as an overview of the included searches, and how they enable the story", - "type": "string" - }, - "original_authors": { - "description": "An array of the original authors of the Analytic Story", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person who originally authored the Analytic Story" - }, - "email": { - "type": "string", - "description": "Email address of the person who originally authored the Analytic Story" - }, - "name": { - "type": "string", - "description": "Name of the person who originally authored the Analytic Story" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "references": { - "description": "An array of URLs that give information about the problem the story is addressing", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "searches": { - "type": "object", - "description": "The different types of searches", - "properties": { - "contextual_searches": { - "description": "The names of the searches used to help scope and provide context to the detection searches", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "detection_searches": { - "description": "The names of the searches used to detect the behaviors detailed within the Analytic Story", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "investigative_searches": { - "description": "The names of the searches used to investigate further after a detection search uncovers events", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "support_searches": { - "description": "The names of the searches that create intermediate data used by the other searches or to provide baseline information to better understand your environment", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false - }, - "spec_version": { - "description": "The version of the Analytic Story specification this manifest follows", - "type": "integer" - }, - "version": { - "description": "The version of the Analytic Story", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "category", - "channel", - "creation_date", - "description", - "id", - "maintainers", - "modification_date", - "narrative", - "original_authors", - "name", - "references", - "searches", - "spec_version", - "version" - ] -} \ No newline at end of file diff --git a/spec/v1/contextual_search.json.spec b/spec/v1/contextual_search.json.spec deleted file mode 100644 index 16bee6a7b0..0000000000 --- a/spec/v1/contextual_search.json.spec +++ /dev/null @@ -1,202 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-04/schema#", - "title": "Contextual Search Manifest", - "description": "The fields that make up the manifest of a version 1 contextual search", - "type": "object", - "properties": { - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Updates are in the ESCU channel", - "type": "string" - }, - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "type": "object", - "description": "Information about the date being ingested", - "properties": { - "data_models": { - "description": "A list of data models, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "type": "array", - "items": { - "enum": ["Apache", "AWS", "Bro", "Microsoft Windows", "Linux", "macOS", "Netbackup", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream", "Active Directory", "Bluecoat", "Carbon Black Response", "Carbon Black Protect", "CrowdStrike Falcon", "Microsoft Exchange", "Nessus", "Palo Alto Firewall", "Qualys" , "Sysmon", "Tanium", "Ziften"] - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false, - "required": ["data_source", "providing_technologies"] - }, - "fields_required": { - "description": "A list of fields that need to be in the result of the detection search for the search to be successful", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person maintaining this search" - }, - "email": { - "type": "string", - "description": "Email address of the person maintaining this search" - }, - "name": { - "type": "string", - "description": "Name of the person maintaining this search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person who originally authored the search" - }, - "email": { - "type": "string", - "description": "Email address of the person who originally authored the search" - }, - "name": { - "type": "string", - "description": "Name of the person who originally authored the search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "search": { - "description": "The search (in SPL) executed within core Splunk", - "type": "string" - }, - "search_description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "search_id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "search_name": { - "description": "The name of the search", - "type": "string" - }, - "search_type": { - "description": "The type of the search", - "enum": ["detection", "investigative", "contextual", "support"] - }, - "search_window": { - "type": "object", - "description": "The fields associated on when this search should run relative to the detection event", - "properties": { - "earliest_time_offset": { - "description": "The number of seconds into the past from the event time the search should cover", - "type": "integer" - }, - "latest_time_offset": { - "description": "The number of seconds into the future from the event time the search should cover", - "type": "integer" - } - }, - "additionalProperties": false, - "required": ["latest_time_offset", "earliest_time_offset"] - }, - "spec_version": { - "description": "The version of the contextual search specification this manifest follows", - "type": "integer" - }, - "status": { - "description": "The current status of the search - development, experimental, production", - "enum": ["development", "experimental", "production"] - }, - "team_notes": { - "description": "Notes for the team about the search", - "type": "string" - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "channel", - "creation_date", - "data_metadata", - "fields_required", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "search", - "search_description", - "search_id", - "search_type", - "search_window", - "spec_version", - "version" - ] -} diff --git a/spec/v1/detection_search.json.spec b/spec/v1/detection_search.json.spec deleted file mode 100644 index 94e8ed943d..0000000000 --- a/spec/v1/detection_search.json.spec +++ /dev/null @@ -1,638 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-04/schema#", - "title": "Detection Search Manifest", - "description": "The fields that make up the manifest of a version 1 detection search", - "type": "object", - "properties": { - "asset_type": { - "description": "Designates the type of asset being investigated", - "type": "string" - }, - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Updates are in the ESCU channel", - "type": "string" - }, - "confidence": { - "description": "Confidence that detected behavior is malicious", - "type": "string" - }, - "correlation_rule": { - "type": "object", - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "type": "object", - "description": "Various fields associated with creating a notable event", - "properties": { - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review", - "type": "string" - }, - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ] - }, - "risk": { - "type": "object", - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "TThe field to which you are assigning risk", - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you’re assigning risk", - "type": "array", - "items": { - "type": "string", - "enum": [ - "system", - "user", - "other" - ] - }, - "minItems": 0, - "maxItems": 1, - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "type": "integer" - } - }, - "additionalProperties": false, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ] - }, - "suppress": { - "type": "object", - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "suppress_fields", - "suppress_period" - ] - } - }, - "additionalProperties": false - }, - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "phantom_playbooks": { - "description": "Array of recommended playbooks", - "type": "array", - "items": { - "type": "object", - "properties": { - "phantom_server": { - "type": "string", - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)" - }, - "playbook_name": { - "type": "string", - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment" - }, - "playbook_display_name": { - "type": "string", - "description": "Display Name of the playbook. Capitalize each letter and remove underscores from playbook_name field. Eg: Simple Network Enrichment" - }, - "playbook_url": { - "type": "string", - "description": "Url of the playbook on Phantom website." - }, - "sensitivity": { - "type": "string", - "description": "TLP colors (White, Green, Amber or Red)" - }, - "severity": { - "type": "string", - "description": "Severity in phantom (High, Medium, Low)" - } - }, - "additionalProperties": false, - "required": [ - "phantom_server", - "playbook_name", - "sensitivity", - "severity", - "playbook_url", - "playbook_display_name" - ] - } - }, - "data_metadata": { - "type": "object", - "description": "Information about the date being ingested", - "properties": { - "data_models": { - "description": "A list of data models, if any, used by this search", - "type": "array", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "type": "array", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery" - ] - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false, - "required": [ - "data_source", - "providing_technologies" - ] - }, - "eli5": { - "description": "Explain it like I’m 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "type": "string" - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "type": "string" - }, - "known_false_positives": { - "description": "Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "type": "array", - "items": { - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person maintaining this search" - }, - "email": { - "type": "string", - "description": "Email address of the person maintaining this search" - }, - "name": { - "type": "string", - "description": "Name of the person maintaining this search" - } - }, - "additionalProperties": false, - "required": [ - "name", - "email", - "company" - ] - } - }, - "mappings": { - "type": "object", - "description": "Mappings to various industry standards and frameworks", - "properties": { - "cis20": { - "description": "A list of critical security controls this search helps you implement", - "type": "array", - "items": { - "enum": [ - "CIS 1", - "CIS 2", - "CIS 3", - "CIS 4", - "CIS 5", - "CIS 6", - "CIS 7", - "CIS 8", - "CIS 9", - "CIS 10", - "CIS 11", - "CIS 12", - "CIS 13", - "CIS 14", - "CIS 15", - "CIS 16", - "CIS 17", - "CIS 18", - "CIS 19", - "CIS 20" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "kill_chain_phases": { - "description": "A list of kill-chain phases to which the search applies", - "type": "array", - "items": { - "enum": [ - "Reconnaissance", - "Weaponization", - "Delivery", - "Exploitation", - "Installation", - "Command and Control", - "Actions on Objectives" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "mitre_attack": { - "description": "A list of the techniques and tactics identified by the search", - "type": "array", - "items": { - "enum": [ - "Initial Access", - "AppInit DLLs", - "Authentication Package", - "Change Default File Association", - "Credential Dumping", - "Application Shimming", - "Account Discovery", - "Accessibility Features", - "Command-Line Interface", - "Execution", - "Persistence", - "Privilege Escalation", - "Defense Evasion", - "Credential Access", - "Discovery", - "Lateral Movement", - "Collection", - "Exfiltration", - "Command and Control", - "Command and Control Protocol", - "Commonly Used Port", - "Custom Cryptographic Protocol", - "DLL Injection", - "DLL Search Order Hijacking", - "DLL Side-Loading", - "Data Compressed", - "Data Encrypted", - "Data Obfuscation", - "Data Staged", - "Data Transfer Size Limits", - "Data from Local System", - "Data from Network Shared Drive", - "Data from Removable Media", - "Disabling Security Tools", - "Email Collection", - "Execution through API", - "Exfiltration Over Alternative Protocol", - "Exfiltration Over Command and Control Channel", - "Exfiltration Over Other Network Medium", - "Exfiltration Over Physical Medium", - "Exploitation of Vulnerability", - "Fallback Channels", - "File Deletion", - "File System Logical Offsets", - "File System Permissions Weakness", - "File and Directory Discovery", - "Graphical User Interface", - "Hypervisor", - "Indicator Blocking", - "Indicator Removal from Tools", - "Indicator Removal on Host", - "Input Capture", - "InstallUtil", - "Legitimate Credentials", - "Local Network Configuration Discovery", - "Local Network Connections Discovery", - "Local Port Monitor", - "Logon Scripts", - "MSBuild", - "Masquerading", - "Modify Existing Service", - "Modify Registry", - "Multi-Stage Channels", - "Multiband Communication", - "Multilayer Encryption", - "NTFS Extended Attributes", - "Network Service Scanning", - "Network Share Connection Removal", - "Network Sniffing", - "New Service", - "Obfuscated Files or Information", - "Pass the Hash", - "Pass the Ticket", - "Path Interception", - "Peripheral Device Discovery", - "Permission Groups Discovery", - "PowerShell", - "Process Discovery", - "Process Hollowing", - "Query Registry", - "Redundant Access", - "Registry Run Keys / Start Folder", - "Regsvcs/Regasm", - "Regsvr32", - "Remote Desktop Protocol", - "Create Account", - "Remote File Copy", - "Remote Services", - "Remote System Discovery", - "Replication Through Removable Media", - "Rootkit", - "Rundll32", - "Scheduled Task", - "Scheduled Transfer", - "Screen Capture", - "Scripting", - "Security Software Discovery", - "Security Support Provider", - "Service Execution", - "Service Registry Permissions Weakness", - "Shared Webroot", - "Shortcut Modification", - "Software Packing", - "Standard Application Layer Protocol", - "Standard Cryptographic Protocol", - "Standard Non-Application Layer Protocol", - "System Information Discovery", - "System Owner/User Discovery", - "System Service Discovery", - "System Time Discovery", - "Taint Shared Content", - "Third-party Software", - "Timestomp", - "Two-Factor Authentication Interception", - "Uncommonly Used Port", - "Video Capture", - "Valid Accounts", - "Web Service", - "Web Shell", - "Windows Admin Shares", - "Windows Management Instrumentation Event Subscription", - "Windows Management Instrumentation", - "Windows Remote Management", - "Winlogon Helper DLL", - "Exploitation for Privilege Escalation" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "nist": { - "description": "A list of the NIST controls the search helps you implement", - "type": "array", - "items": { - "enum": [ - "ID.AM", - "ID.RA", - "PR.DS", - "PR.IP", - "PR.AC", - "PR.PT", - "PR.AT", - "PR.MA", - "DE.CM", - "DE.DP", - "DE.AE", - "RS.MI", - "RS.AN", - "RS.RP", - "RS.IM", - "RS.CO", - "RC.IM", - "RC.CO" - ] - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "type": "array", - "items": { - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person who originally authored the search" - }, - "email": { - "type": "string", - "description": "Email address of the person who originally authored the search" - }, - "name": { - "type": "string", - "description": "Name of the person who originally authored the search" - } - }, - "additionalProperties": false, - "required": [ - "name", - "email", - "company" - ] - } - }, - "references": { - "description": "A list of URLs that give more information about the search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "scheduling": { - "type": "object", - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "additionalProperties": false - }, - "search": { - "description": "The search (in SPL) executed within core Splunk", - "type": "string" - }, - "search_description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "search_id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "search_name": { - "description": "The name of the search", - "type": "string" - }, - "search_type": { - "description": "The type of the search", - "enum": [ - "detection", - "investigative", - "contextual", - "support" - ] - }, - "security_domain": { - "description": "The high-level security area to which the search belongs", - "enum": [ - "access", - "endpoint", - "network", - "threat" - ] - }, - "spec_version": { - "description": "The version of the detection search specification this manifest follows", - "type": "integer" - }, - "status": { - "description": "The current status of the search - development, experimental, production", - "enum": [ - "development", - "experimental", - "production" - ] - }, - "team_notes": { - "description": "Notes for the team about the search", - "type": "string" - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "channel", - "confidence", - "creation_date", - "data_metadata", - "eli5", - "how_to_implement", - "known_false_positives", - "maintainers", - "modification_date", - "original_authors", - "search", - "search_description", - "search_id", - "search_type", - "security_domain", - "scheduling", - "version" - ] -} \ No newline at end of file diff --git a/spec/v1/investigative_search.json.spec b/spec/v1/investigative_search.json.spec deleted file mode 100644 index 908809e8bd..0000000000 --- a/spec/v1/investigative_search.json.spec +++ /dev/null @@ -1,220 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-04/schema#", - "title": "Investigative Search Manifest", - "description": "The fields that make up the manifest of a version 1 investigative earch", - "type": "object", - "properties": { - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Updates are in the ESCU channel", - "type": "string" - }, - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "type": "object", - "description": "Information about the date being ingested", - "properties": { - "data_models": { - "description": "A list of data models, if any, used by this search", - "type": "array", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "type": "array", - "items": { - "enum": ["Apache", "AWS", "Bro", "Microsoft Windows", "Linux", "macOS", "Netbackup", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream", "Active Directory", "Bluecoat", "Carbon Black Response", "Carbon Black Protect", "CrowdStrike Falcon", "Microsoft Exchange", "Nessus", "Palo Alto Firewall", "Qualys" , "Sysmon", "Tanium", "Ziften"] - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false, - "required": ["data_source", "providing_technologies"] - }, - "fields_required": { - "description": "A list of fields that need to be in the result of the detection search for the search to be successful", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person maintaining this search" - }, - "email": { - "type": "string", - "description": "Email address of the person maintaining this search" - }, - "name": { - "type": "string", - "description": "Name of the person maintaining this search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person who originally authored the search" - }, - "email": { - "type": "string", - "description": "Email address of the person who originally authored the search" - }, - "name": { - "type": "string", - "description": "Name of the person who originally authored the search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "search": { - "description": "The search (in SPL) executed within core Splunk", - "type": "string" - }, - "search_description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "search_id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "search_name": { - "description": "The name of the search", - "type": "string" - }, - "search_type": { - "description": "The type of the search", - "enum": ["detection", "investigative", "contextual", "support"] - }, - "search_window": { - "type": "object", - "description": "The fields associated on when this search should run relative to the detection event", - "properties": { - "earliest_time_offset": { - "description": "The number of seconds into the past from the event time the search should cover", - "type": "integer" - }, - "latest_time_offset": { - "description": "The number of seconds into the future from the event time the search should cover", - "type": "integer" - } - }, - "additionalProperties": false, - "required": ["latest_time_offset", "earliest_time_offset"] - }, - "spec_version": { - "description": "The version of the investigative search specification this manifest follows", - "type": "integer" - }, - "status": { - "description": "The current status of the search - development, experimental, production", - "enum": ["development", "experimental", "production"] - }, - "team_notes": { - "description": "Notes for the team about the search", - "type": "string" - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "channel", - "creation_date", - "data_metadata", - "fields_required", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "search", - "search_description", - "search_id", - "search_type", - "search_window", - "spec_version", - "version" - ] -} diff --git a/spec/v1/support_search.json.spec b/spec/v1/support_search.json.spec deleted file mode 100644 index fdeee988b6..0000000000 --- a/spec/v1/support_search.json.spec +++ /dev/null @@ -1,218 +0,0 @@ -{ - "$schema": "http://json-schema.org/draft-04/schema#", - "title": "Support Search Manifest", - "description": "The fields that make up the manifest of a version 1 support search", - "type": "object", - "properties": { - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Updates are in the ESCU channel", - "type": "string" - }, - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "type": "object", - "description": "Information about the date being ingested", - "properties": { - "data_models": { - "description": "A list of data models, if any, used by this search", - "type": "array", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "type": "array", - "items": { - "enum": ["Apache", "AWS", "Bro", "Microsoft Windows", "Linux", "macOS", "Netbackup", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Stream", "Active Directory", "Bluecoat", "Carbon Black Response", "Carbon Black Protect", "CrowdStrike Falcon", "Microsoft Exchange", "Nessus", "Palo Alto Firewall", "Qualys" , "Sysmon", "Tanium", "Ziften"] - }, - "minItems": 0, - "uniqueItems": true - } - }, - "additionalProperties": false, - "required": ["data_source", "providing_technologies"] - }, - "eli5": { - "description": "Explain it like I’m 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "type": "string" - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person maintaining this search" - }, - "email": { - "type": "string", - "description": "Email address of the person maintaining this search" - }, - "name": { - "type": "string", - "description": "Name of the person maintaining this search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "type": "array", - "items" :{ - "type": "object", - "properties": { - "company": { - "type": "string", - "description": "Company associated with the person who originally authored the search" - }, - "email": { - "type": "string", - "description": "Email address of the person who originally authored the search" - }, - "name": { - "type": "string", - "description": "Name of the person who originally authored the search" - } - }, - "additionalProperties": false, - "required": ["name", "email", "company"] - } - }, - "scheduling": { - "type": "object", - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time the search should run against in Splunk format", - "type": "string" - } - }, - "additionalProperties": false - }, - "search": { - "description": "The search (in SPL) executed within core Splunk", - "type": "string" - }, - "search_description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "search_id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "search_name": { - "description": "The name of the search", - "type": "string" - }, - "search_type": { - "description": "The type of the search", - "enum": ["detection", "investigative", "contextual", "support"] - }, - "spec_version": { - "description": "The version of the detection search specification this manifest follows", - "type": "integer" - }, - "status": { - "description": "The current status of the search - development, experimental, production", - "enum": ["development", "experimental", "production"] - }, - "team_notes": { - "description": "Notes for the team about the search", - "type": "string" - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "channel", - "creation_date", - "data_metadata", - "eli5", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "scheduling", - "search", - "search_description", - "search_id", - "search_type", - "spec_version", - "version" - ] -} \ No newline at end of file diff --git a/spec/v2/baselines.spec.json b/spec/v2/baselines.spec.json deleted file mode 100644 index b4d1eab4b1..0000000000 --- a/spec/v2/baselines.spec.json +++ /dev/null @@ -1,499 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/baselines.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)", - "examples": [ - "automation (hostname)" - ], - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment", - "examples": [ - "community/dns_hijack_investigation" - ], - "type": "string" - }, - "playbook_url": { - "description": "Url of the playbook on Phantom website.", - "examples": [ - "https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" - ], - "type": "string" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber or Red)", - "examples": [ - "green" - ], - "type": "string" - }, - "severity": { - "description": "Severity in phantom (High, Medium, Low)", - "examples": [ - "medium" - ], - "type": "string" - } - }, - "required": [ - "phantom_server", - "playbook_name", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "properties": { - "lookups": { - "description": "A list of lookups in the search", - "examples": [ - "detection_search_lookup" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "macros": { - "description": "A list of macros in the search", - "examples": [ - "detection_search_output_filter" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within core Splunk for investgation.", - "examples": [ - "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)" - ], - "type": "string" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - } - }, - "description": "The fields that make up the manifest of a version 2 baseline search", - "properties": { - "baseline": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - } - }, - "type": "object" - }, - "creation_date": { - "description": "The date the baseline manifest was created", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "data_metadata": { - "description": "Information about the date being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "examples": [ - "wineventlog" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "examples": [ - "DNS" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "examples": [ - "stream:dns" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Azure", - "Bro", - "GCP", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "Passive Total", - "WHOIS", - "Okta" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the search is is doing to create a baseline", - "examples": [ - "The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Traffic` datamodel, then stores the output under the `discovered_dns_records.csv` lookup" - ], - "type": "string" - }, - "eli5": { - "description": "Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "examples": [ - "Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`" - ], - "type": "string" - }, - "entities": { - "description": "A list of entities that will used in the story flow or are relevant to the security investigation.", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "parent_process_id", - "process_id", - "process_name", - "process", - "recipient", - "Region", - "region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query", - "answer", - "domain" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "examples": [ - "To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation" - ], - "type": "string" - }, - "id": { - "description": "The unique identifier for the search", - "examples": [ - "c096f721-8842-42ce-bfc7-74bd8c72b7c3" - ], - "type": "string" - }, - "known_false_positives": { - "description": "Describe the known false postives while the analyst builds the baseline.", - "examples": [ - "Please vet the lookup created by this baseline search." - ], - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "items": { - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "name": { - "description": "The name of the search that creates the baseline", - "examples": [ - "Discover DNS records" - ], - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "spec_version": { - "description": "The version of the detection search specification this manifest follows", - "examples": [ - "2.0" - ], - "type": "integer" - }, - "type": { - "description": "The type of baseline", - "enum": [ - "splunk", - "phantom", - "uba" - ], - "examples": [ - "splunk" - ] - }, - "version": { - "description": "The version of the search", - "examples": [ - "1", - "2" - ], - "type": "string" - } - }, - "required": [ - "id", - "creation_date", - "data_metadata", - "eli5", - "known_false_positives", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "name", - "description", - "type", - "spec_version", - "version", - "baseline" - ], - "title": "Baseline Manifest", - "type": "object" -} diff --git a/spec/v2/detections.spec.json b/spec/v2/detections.spec.json deleted file mode 100644 index f6d4f4d3c3..0000000000 --- a/spec/v2/detections.spec.json +++ /dev/null @@ -1,1526 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/detections.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "examples": [ - "src" - ], - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "examples": [ - "The table represents a list of DNS records and their responses for corporate domains that have recently changed" - ], - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review. Add tokenized fieldnames", - "examples": [ - "DNS record changed on $src$" - ], - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "TThe field to which you are assigning risk", - "examples": [ - "src" - ], - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "examples": [ - "system" - ], - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "examples": [ - "40" - ], - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "examples": [ - "0 * * * *" - ], - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "examples": [ - "-70m@m" - ], - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "examples": [ - "-10m@m" - ], - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search that will trigger phantom playbook", - "examples": [ - "| sendalert runphantomplaybook" - ], - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "examples": [ - "src" - ], - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "examples": [ - "28800s" - ], - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "phantom_server": { - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)", - "examples": [ - "automation (hostname)" - ], - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment", - "examples": [ - "community/dns_hijack_detect_playbook" - ], - "type": "string" - }, - "playbook_url": { - "description": "Url of the playbook on Phantom website.", - "examples": [ - "https://my.phantom.us/4.1/playbook/dns-hijack-investigation/" - ], - "type": "string" - }, - "sensitivity": { - "description": "TLP colors (white, green, amber or red)", - "examples": [ - "green" - ], - "type": "string" - }, - "severity": { - "description": "Severity in phantom (High, Medium, Low)", - "examples": [ - "high" - ], - "type": "string" - } - }, - "required": [ - "correlation_rule", - "playbook_name", - "phantom_server", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "lookups": { - "description": "A list of lookups in the search", - "examples": [ - "detection_search_lookup" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "macros": { - "description": "A list of macros in the search", - "examples": [ - "detection_search_output_filter" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "drilldown_name": { - "description": "The text for the drilldown hyperlink within the Incident Review dashboard", - "type": "string" - }, - "drilldown_search": { - "description": "The actual drilldown search", - "type": "string" - }, - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review", - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "TThe field to which you are assigning risk", - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search that will trigger phantom playbook", - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "type": "object" - }, - "uba": { - "properties": { - "correlation_rule": { - "description": "Various fields to enhance usability in Enterprise Security", - "properties": { - "notable": { - "description": "Various fields associated with creating a notable event", - "properties": { - "nes_fields": { - "description": "A list of suggested fields to be used for notable-event suppression", - "type": "string" - }, - "rule_description": { - "description": "Description of the notable event that will display in Incident Review", - "type": "string" - }, - "rule_title": { - "description": "Title of the notable event that will display in Incident Review", - "type": "string" - } - }, - "required": [ - "rule_description", - "rule_title", - "nes_fields" - ], - "type": "object" - }, - "risk": { - "description": "Fields associated with assigning risk to objects", - "properties": { - "risk_object": { - "description": "TThe field to which you are assigning risk", - "type": "string" - }, - "risk_object_type": { - "description": "The type of object to which you are assigning risk", - "items": { - "enum": [ - "system", - "user", - "other" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "risk_score": { - "description": "Score assigned to risk_object", - "type": "integer" - } - }, - "required": [ - "risk_score", - "risk_object", - "risk_object_type" - ], - "type": "object" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within core Splunk", - "type": "string" - }, - "suppress": { - "description": "Fields associated with suppressing the creation of multiple alerts", - "properties": { - "suppress_fields": { - "description": "The fields to base the suppression on", - "type": "string" - }, - "suppress_period": { - "description": "The length of time the suppression should be in effect", - "type": "string" - } - }, - "required": [ - "suppress_fields", - "suppress_period" - ], - "type": "object" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - }, - "event_type": { - "description": "An anomaly or threat.", - "examples": [ - "anomaly" - ], - "type": "string" - }, - "model": { - "description": "The name of the Splunk UBA model that detected the anomaly.", - "examples": [ - "UBA Model" - ], - "type": "string" - }, - "model_version": { - "description": "Url of the playbook on Phantom website.", - "examples": [ - "2.0" - ], - "type": "string" - }, - "threat_category": { - "description": "The category of a threat in Splunk UBA.", - "examples": [ - "Malware" - ], - "type": "string" - } - }, - "required": [ - "correlation_rule", - "event_type", - "model", - "model_version", - "threat_category" - ], - "type": "object" - } - }, - "description": "A object that defines the parameters for detecting things using various Splunk capabilities", - "properties": { - "asset_type": { - "description": "Designates the type of asset being investigated", - "examples": [ - "Endpoint" - ], - "type": "string" - }, - "baselines": { - "description": "An array of the baseline objects to exectute before the detection ", - "items": { - "properties": { - "id": { - "description": "UUID of the baseline object", - "examples": [ - "c096f721-8842-42ce-bfc7-74bd8c72b7c3" - ], - "type": "string" - }, - "name": { - "description": "name of baseline object", - "examples": [ - "Discover DNS records" - ], - "type": "string" - }, - "type": { - "description": "Type of baseline to execute", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "splunk" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "confidence": { - "description": "Confidence that detected behavior is malicious", - "enum": [ - "high", - "medium", - "low" - ], - "examples": [ - "high" - ] - }, - "creation_date": { - "description": "The date the story manifest was created", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "data_metadata": { - "description": "Information about the date being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "examples": [ - "wineventlog" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "examples": [ - "Network_Resolution" - ], - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint", - "UEBA" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "examples": [ - "DNS" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "examples": [ - "stream:dns" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "examples": [ - "Bro" - ], - "items": { - "enum": [ - "Apache", - "AWS", - "Azure", - "Bro", - "GCP", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "OSquery", - "Okta" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the detection is designed to find", - "examples": [ - "The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day." - ], - "type": "string" - }, - "detect": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - }, - { - "required": [ - "uba" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - }, - "uba": { - "$ref": "#/definitions/uba" - } - }, - "type": "object" - }, - "eli5": { - "description": "Explain it like I am 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "examples": [ - "Using a lookup `discover_dns_records` generated by support search \"Discover DNS records\" we check previous network traffic and make sure the responses have not changed." - ], - "type": "string" - }, - "entities": { - "description": "A list of entities that is outputed by the search...", - "examples": [ - "dest", - "user" - ], - "items": { - "enum": [ - "accessKeyId", - "app", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "parent_process_id", - "parent_process_name", - "process_id", - "process_name", - "process", - "recipient", - "Region", - "region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query", - "host", - "dest_ip", - "userName", - "dest_user", - "url", - "domain", - "cluster_name" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "examples": [], - "type": "string" - }, - "id": { - "description": "The unique identifier for the detection", - "examples": [ - "To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search \"Discover DNS record\"." - ], - "type": "string" - }, - "investigations": { - "description": "An array of the investigation objects to exectute on the detection results ", - "items": { - "properties": { - "id": { - "description": "UUID of the investigation object", - "examples": [ - "bc11a8cf-35e7-4bb2-8140-e756cc06fd72" - ], - "type": "string" - }, - "name": { - "description": "Name of investigation object ", - "examples": [ - "Get DNS Server History for a host" - ], - "type": "string" - }, - "type": { - "description": "Type of investigation object", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "splunk" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "known_false_positives": { - "description": "Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior", - "examples": [ - "Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate." - ], - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "items": { - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "mappings": { - "description": "Mappings to various industry standards and frameworks", - "properties": { - "cis20": { - "description": "A list of critical security controls this search helps you implement", - "examples": [ - "CIS 12" - ], - "items": { - "enum": [ - "CIS 1", - "CIS 2", - "CIS 3", - "CIS 4", - "CIS 5", - "CIS 6", - "CIS 7", - "CIS 8", - "CIS 9", - "CIS 10", - "CIS 11", - "CIS 12", - "CIS 13", - "CIS 14", - "CIS 15", - "CIS 16", - "CIS 17", - "CIS 18", - "CIS 19", - "CIS 20" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "kill_chain_phases": { - "description": "A list of kill-chain phases to which the search applies", - "examples": [ - "Reconnaissance" - ], - "items": { - "enum": [ - "Reconnaissance", - "Weaponization", - "Delivery", - "Exploitation", - "Installation", - "Command and Control", - "Actions on Objectives" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "mitre_attack": { - "description": "A list of the techniques and tactics identified by the search", - "examples": [ - "Defense Evasion", - "Initial Access" - ], - "items": { - "enum": [ - "Initial Access", - "Impact", - "Execution", - "Persistence", - "Spearphishing Attachment", - "Spearphishing Link", - "Privilege Escalation", - "Defense Evasion", - "Credential Access", - "Discovery", - "Lateral Movement", - "Collection", - "Exfiltration", - "Accessibility Features", - "Account Discovery", - "AppInit DLLs", - "Application Shimming", - "Authentication Package", - "Change Default File Association", - "Command and Control", - "Command and Control Protocol", - "Command-Line Interface", - "Commonly Used Port", - "Credential Dumping", - "Custom Cryptographic Protocol", - "DLL Injection", - "DLL Search Order Hijacking", - "DLL Side-Loading", - "Data Compressed", - "Data Encrypted", - "Data Obfuscation", - "Data Staged", - "Data Transfer Size Limits", - "Data from Local System", - "Data from Network Shared Drive", - "Data from Removable Media", - "Disabling Security Tools", - "Email Collection", - "Execution through API", - "Exfiltration Over Alternative Protocol", - "Exfiltration Over Command and Control Channel", - "Exfiltration Over Other Network Medium", - "Exfiltration Over Physical Medium", - "Exploitation of Vulnerability", - "Fallback Channels", - "File Deletion", - "File System Logical Offsets", - "File System Permissions Weakness", - "File and Directory Discovery", - "Graphical User Interface", - "Hypervisor", - "Indicator Blocking", - "Indicator Removal from Tools", - "Indicator Removal on Host", - "Input Capture", - "InstallUtil", - "Legitimate Credentials", - "Local Network Configuration Discovery", - "Local Network Connections Discovery", - "Local Port Monitor", - "Logon Scripts", - "MSBuild", - "Masquerading", - "Modify Existing Service", - "Modify Registry", - "Multi-Stage Channels", - "Multiband Communication", - "Multilayer Encryption", - "NTFS Extended Attributes", - "Network Service Scanning", - "Network Share Connection Removal", - "Network Sniffing", - "New Service", - "Obfuscated Files or Information", - "Pass the Hash", - "Pass the Ticket", - "Path Interception", - "Peripheral Device Discovery", - "Permission Groups Discovery", - "PowerShell", - "Process Discovery", - "Process Hollowing", - "Query Registry", - "Redundant Access", - "Registry Run Keys / Start Folder", - "Regsvcs/Regasm", - "Regsvr32", - "Remote Desktop Protocol", - "Create Account", - "Remote File Copy", - "Remote Services", - "Remote System Discovery", - "Replication Through Removable Media", - "Rootkit", - "Rundll32", - "Scheduled Task", - "Scheduled Transfer", - "Screen Capture", - "Scripting", - "Security Software Discovery", - "Security Support Provider", - "Service Execution", - "Service Registry Permissions Weakness", - "Shared Webroot", - "Shortcut Modification", - "Software Packing", - "Standard Application Layer Protocol", - "Standard Cryptographic Protocol", - "Standard Non-Application Layer Protocol", - "System Information Discovery", - "System Owner/User Discovery", - "System Service Discovery", - "System Time Discovery", - "Taint Shared Content", - "Third-party Software", - "Timestomp", - "Two-Factor Authentication Interception", - "Uncommonly Used Port", - "Video Capture", - "Valid Accounts", - "Web Service", - "Web Shell", - "Windows Admin Shares", - "Windows Management Instrumentation Event Subscription", - "Windows Management Instrumentation", - "Windows Remote Management", - "Winlogon Helper DLL", - "Exploitation for Privilege Escalation" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "mitre_technique_id": { - "description": "MITRE Technique ID identified by the search", - "examples": [ - "T1001", - "T1002", - "T1003" - ], - "items": { - "enum": [ - "T1001", - "T1002", - "T1003", - "T1004", - "T1005", - "T1006", - "T1007", - "T1008", - "T1009", - "T1010", - "T1011", - "T1012", - "T1013", - "T1014", - "T1015", - "T1016", - "T1017", - "T1018", - "T1019", - "T1020", - "T1021", - "T1022", - "T1023", - "T1024", - "T1025", - "T1026", - "T1027", - "T1028", - "T1029", - "T1030", - "T1031", - "T1032", - "T1033", - "T1034", - "T1035", - "T1036", - "T1037", - "T1038", - "T1039", - "T1040", - "T1041", - "T1042", - "T1043", - "T1044", - "T1045", - "T1046", - "T1047", - "T1048", - "T1049", - "T1050", - "T1051", - "T1052", - "T1053", - "T1054", - "T1055", - "T1056", - "T1057", - "T1058", - "T1059", - "T1060", - "T1061", - "T1062", - "T1063", - "T1064", - "T1065", - "T1066", - "T1067", - "T1068", - "T1069", - "T1070", - "T1071", - "T1072", - "T1073", - "T1074", - "T1075", - "T1076", - "T1077", - "T1078", - "T1079", - "T1080", - "T1081", - "T1082", - "T1083", - "T1084", - "T1085", - "T1086", - "T1087", - "T1088", - "T1089", - "T1090", - "T1091", - "T1092", - "T1093", - "T1094", - "T1095", - "T1096", - "T1097", - "T1098", - "T1099", - "T1100", - "T1101", - "T1102", - "T1103", - "T1104", - "T1105", - "T1106", - "T1107", - "T1108", - "T1109", - "T1110", - "T1111", - "T1112", - "T1113", - "T1114", - "T1115", - "T1116", - "T1117", - "T1118", - "T1119", - "T1120", - "T1121", - "T1122", - "T1123", - "T1124", - "T1125", - "T1126", - "T1127", - "T1128", - "T1129", - "T1130", - "T1131", - "T1132", - "T1133", - "T1134", - "T1135", - "T1136", - "T1137", - "T1138", - "T1139", - "T1140", - "T1141", - "T1142", - "T1143", - "T1144", - "T1145", - "T1146", - "T1147", - "T1148", - "T1149", - "T1150", - "T1151", - "T1152", - "T1153", - "T1154", - "T1155", - "T1156", - "T1157", - "T1158", - "T1159", - "T1160", - "T1161", - "T1162", - "T1163", - "T1164", - "T1165", - "T1166", - "T1167", - "T1168", - "T1169", - "T1170", - "T1171", - "T1172", - "T1173", - "T1174", - "T1175", - "T1176", - "T1177", - "T1178", - "T1179", - "T1180", - "T1181", - "T1182", - "T1183", - "T1184", - "T1185", - "T1186", - "T1187", - "T1188", - "T1189", - "T1190", - "T1191", - "T1192", - "T1193", - "T1194", - "T1195", - "T1196", - "T1197", - "T1198", - "T1199", - "T1200", - "T1201", - "T1202", - "T1203", - "T1204", - "T1205", - "T1206", - "T1207", - "T1208", - "T1209", - "T1210", - "T1211", - "T1212", - "T1213", - "T1214", - "T1215", - "T1216", - "T1217", - "T1218", - "T1219", - "T1220", - "T1221", - "T1222", - "T1223", - "T1480", - "T1482", - "T1483", - "T1484", - "T1485", - "T1486", - "T1487", - "T1488", - "T1489", - "T1490", - "T1491", - "T1492", - "T1493", - "T1494", - "T1495", - "T1496", - "T1497", - "T1498", - "T1499", - "T1500", - "T1501", - "T1502", - "T1503", - "T1504", - "T1505", - "T1506", - "T1514", - "T1518", - "T1519", - "T1522", - "T1525", - "T1526", - "T1527", - "T1528", - "T1529", - "T1530", - "T1531", - "T1534", - "T1535", - "T1536", - "T1537", - "T1538", - "T1539" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "nist": { - "description": "A list of the NIST controls the search helps you implement", - "examples": [ - "ID.AM", - "PR.PT" - ], - "items": { - "enum": [ - "ID.AM", - "ID.RA", - "PR.DS", - "PR.IP", - "PR.AC", - "PR.PT", - "PR.AT", - "PR.MA", - "DE.CM", - "DE.DP", - "DE.AE", - "RS.MI", - "RS.AN", - "RS.RP", - "RS.IM", - "RS.CO", - "RC.IM", - "RC.CO" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "type": "object" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "examples": [ - "2019-02-14" - ], - "type": "string" - }, - "name": { - "description": "The name of the detection", - "examples": [ - "DNS record changed" - ], - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "references": { - "description": "A list of URLs that give more information about the search", - "examples": [ - "https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "responses": { - "description": "An array of the response objects to exectute on the detection results", - "items": { - "properties": { - "id": { - "description": "UUID of the Respose object", - "examples": [ - "1169w17b-ef78-4b59-aae8-5369073014e1" - ], - "type": "string" - }, - "name": { - "description": "Name of Response Object", - "examples": [ - "DNS Hijack Reponse" - ], - "type": "string" - }, - "type": { - "description": "Type of baseline to execute", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "examples": [ - "phantom" - ], - "type": "string" - } - }, - "required": [ - "type", - "name", - "id" - ], - "type": "object" - }, - "type": "array" - }, - "security_domain": { - "description": "The high-level security area to which the search belongs", - "enum": [ - "access", - "endpoint", - "network", - "threat", - "cloud" - ], - "examples": [ - "endpoint" - ] - }, - "spec_version": { - "description": "The version of the detection specification this manifest follows", - "examples": [ - "2.0" - ], - "type": "integer" - }, - "type": { - "description": "The type of detection", - "enum": [ - "uba", - "splunk", - "phantom" - ], - "examples": [ - "phantom" - ] - }, - "version": { - "description": "The version of the detection", - "examples": [ - "1" - ], - "type": "string" - } - }, - "required": [ - "confidence", - "creation_date", - "data_metadata", - "eli5", - "how_to_implement", - "known_false_positives", - "maintainers", - "modification_date", - "original_authors", - "description", - "id", - "type", - "security_domain", - "version", - "detect" - ], - "title": "Detection Manifest", - "type": "object" -} diff --git a/spec/v2/investigations.spec.json b/spec/v2/investigations.spec.json deleted file mode 100644 index bf07e97947..0000000000 --- a/spec/v2/investigations.spec.json +++ /dev/null @@ -1,401 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/investigations.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)", - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment", - "type": "string" - }, - "playbook_url": { - "description": "Url of the playbook on Phantom website.", - "type": "string" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber or Red)", - "type": "string" - }, - "severity": { - "description": "Severity in phantom (High, Medium, Low)", - "type": "string" - } - }, - "required": [ - "playbook_name", - "phantom_server", - "playbook_url" - ], - "type": "object" - }, - "splunk": { - "properties": { - "fields_required": { - "description": "A list of data models, if any, used by this search", - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "lookups": { - "description": "A list of lookups in the search", - "examples": [ - "detection_search_lookup" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "macros": { - "description": "A list of macros in the search", - "examples": [ - "detection_search_output_filter" - ], - "items": { - "type": "string" - }, - "type": "array" - }, - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "The search (in SPL) executed within core Splunk for investgation.", - "type": "string" - } - }, - "required": [ - "search", - "fields_required", - "schedule" - ], - "type": "object" - } - }, - "description": "The fields that make up the manifest of a version 2 investigative object", - "properties": { - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "description": "Information about the date being ingested", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "items": { - "enum": [ - "Alerts", - "Application_State", - "Authentication", - "Certificates", - "Change_Analysis", - "Change", - "Cloud_Infrastructure", - "Malware", - "Email", - "Identity_Management", - "Network_Resolution", - "Network_Traffic", - "Vulnerabilities", - "Web", - "Network_Sessions", - "Updates", - "Risk", - "Endpoint" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Azure", - "Bro", - "GCP", - "Microsoft Windows", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "OSquery", - "SMTP", - "Cuckoo", - "VirusTotal", - "DeepSight", - "Okta" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what the search is designed to detect", - "type": "string" - }, - "eli5": { - "description": "Explain it like I\u2019m 5 - A detail description of the SPL of the search, written in a style that can be understood by a future Splunk expert", - "type": "string" - }, - "entities": { - "description": "A list of entities that will used in the story flow or are relevant to the security investigation. ", - "items": { - "enum": [ - "accessKeyId", - "arn", - "app", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "parent_process_id", - "process_id", - "process_name", - "process", - "recipient", - "Region", - "region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "dest_ip", - "domain" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this search, from what needs to be ingested, config files modified, and suggested per site modifications", - "type": "string" - }, - "id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "investigate": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - } - }, - "type": "object" - }, - "known_false_positives": { - "description": "Scenarios in which detected behavior is benig, coupled with suggestions on how to verify the behavior", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "name": { - "description": "The name of the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the search", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "spec_version": { - "description": "The version of the investigative search specification this manifest follows", - "type": "integer" - }, - "type": { - "description": "Type of product that will support this investigate object.", - "enum": [ - "phantom", - "splunk", - "uba" - ] - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "required": [ - "creation_date", - "data_metadata", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "description", - "id", - "type", - "spec_version", - "version", - "investigate" - ], - "title": "Investigative Search Manifest", - "type": "object" -} diff --git a/spec/v2/macros.spec.json b/spec/v2/macros.spec.json deleted file mode 100644 index 48c5a2f37f..0000000000 --- a/spec/v2/macros.spec.json +++ /dev/null @@ -1,43 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/macros.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "description": "An object that defines the parameters for a Splunk Macro", - "properties": { - "arguments": { - "description": "A list of the arguments being passed to this macro", - "type": "array", - "items": { - "type": "string" - }, - "minItems": 0, - "uniqueItems": true - }, - "name": { - "description": "The name of the macro", - "examples": [ - "detection_search_output_filter" - ], - "type": "string" - }, - "definition": { - "description": "The macro definition", - "examples": [ - "(query=fls-na* AND query = www* AND query=images*)" - ], - "type": "string" - }, - "description": { - "description": "What the macro is intended to filter", - "examples": [ - "Use this macro to filter out known good objects" - ], - "type": "string" - } - }, - "required": [ - "name", - "description" - ], - "title": "Macro Manifest", - "type": "object" -} diff --git a/spec/v2/responses.spec.json b/spec/v2/responses.spec.json deleted file mode 100644 index 887209b025..0000000000 --- a/spec/v2/responses.spec.json +++ /dev/null @@ -1,340 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/responses.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "definitions": { - "phantom": { - "properties": { - "phantom_server": { - "description": "IP address and username of the phantom server. Currently, we will ship this value as automation (hostname) and we encourage the users to modify those values according to their environment. Eg: automation (hostname)", - "type": "string" - }, - "playbook_display_name": { - "description": "Display Name of the playbook. Capitalize each letter and remove underscores from playbook_name field. Eg: Simple Network Enrichment", - "type": "string" - }, - "playbook_name": { - "description": "Name of the playbook. This name should be the same as the name on phantom community repository on github with underscores and appended with community/. The playbooks are hosted on https://github.com/phantomcyber/playbooks. Eg: community/simple_network_enrichment", - "type": "string" - }, - "playbook_url": { - "description": "Url of the playbook on Phantom website.", - "type": "string" - }, - "sensitivity": { - "description": "TLP colors (White, Green, Amber or Red)", - "type": "string" - }, - "severity": { - "description": "Severity in phantom (High, Medium, Low)", - "type": "string" - }, - "type": { - "description": "Type of baseline to execute", - "enum": [ - "phantom", - "splunk", - "uba" - ], - "type": "string" - } - }, - "type": "object" - }, - "splunk": { - "properties": { - "schedule": { - "description": "Various fields to assist in scheduling the search", - "properties": { - "cron_schedule": { - "description": "Schedule of the search in cron format", - "type": "string" - }, - "earliest_time": { - "description": "The earliest time the search should run in Splunk format", - "type": "string" - }, - "latest_time": { - "description": "The latest time tes search should run against in Splunk format", - "type": "string" - } - }, - "type": "object" - }, - "search": { - "description": "A reponse action exectued in splunk", - "type": "string" - } - }, - "required": [ - "search", - "schedule" - ], - "type": "object" - } - }, - "description": "The fields that make up the manifest of a version 1 response spec", - "properties": { - "creation_date": { - "description": "The date the story manifest was created", - "type": "string" - }, - "data_metadata": { - "additionalProperties": false, - "description": "Information about the date being used to run the response", - "properties": { - "data_eventtypes": { - "description": "A list of eventtypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_models": { - "description": "A list of data models, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_source": { - "description": "A high-level description of the type of data needed for this search to complete", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "data_sourcetypes": { - "description": "The list of sourcetypes, if any, used by this search", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "providing_technologies": { - "description": "A list of technologies that provide this data", - "items": { - "enum": [ - "Apache", - "AWS", - "Bro", - "Microsoft Windows", - "Censys", - "Linux", - "macOS", - "Netbackup", - "Splunk Enterprise", - "Splunk Enterprise Security", - "Splunk Stream", - "Active Directory", - "Bluecoat", - "Carbon Black Response", - "Carbon Black Protect", - "CrowdStrike Falcon", - "Microsoft Exchange", - "Nessus", - "Palo Alto Firewall", - "Qualys", - "Sysmon", - "Tanium", - "Ziften", - "Censys", - "Passive Total", - "WHOIS" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - } - }, - "required": [ - "data_source", - "providing_technologies" - ], - "type": "object" - }, - "description": { - "description": "A description of what this reponse object will do ", - "type": "string" - }, - "entities": { - "description": "A list of entities that is either an input or an output for the security workflow.", - "items": { - "enum": [ - "accessKeyId", - "arn", - "awsRegion", - "bucketName", - "City", - "Country", - "dest_port", - "dest", - "event_id", - "instanceId", - "message_id", - "networkAclId", - "process_name", - "process", - "recipient", - "Region", - "resourceId", - "session_id", - "src_ip", - "src_mac", - "src_user", - "src", - "user", - "query", - "answer", - "domain" - ] - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "fields_required": { - "description": "A list of fields that need to be in the result of the detection search for the search to be successful", - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "how_to_implement": { - "description": "A discussion on how to implement this reponse object, the config files, etc", - "type": "string" - }, - "id": { - "description": "The unique identifier for the search", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the reponse spec", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person maintaining this search", - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this search", - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to the search", - "type": "string" - }, - "name": { - "description": "The name of the search", - "type": "string" - }, - "original_authors": { - "description": "A list of the original authors of the reponse object", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person who originally authored the search", - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the search", - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the search", - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "response": { - "anyOf": [ - { - "required": [ - "splunk" - ] - }, - { - "required": [ - "phantom" - ] - } - ], - "properties": { - "phantom": { - "$ref": "#/definitions/phantom" - }, - "splunk": { - "$ref": "#/definitions/splunk" - } - }, - "type": "object" - }, - "spec_version": { - "description": "The version of the investigative search specification this manifest follows", - "type": "integer" - }, - "type": { - "description": "The type of detection", - "enum": [ - "splunk", - "phantom" - ] - }, - "version": { - "description": "The version of the search", - "type": "string" - } - }, - "required": [ - "creation_date", - "data_metadata", - "entity", - "how_to_implement", - "maintainers", - "modification_date", - "original_authors", - "search", - "description", - "id", - "type", - "spec_version", - "version", - "response" - ], - "title": "Response Manifest", - "type": "object" -} diff --git a/spec/v2/story.spec.json b/spec/v2/story.spec.json deleted file mode 100644 index b5141f9388..0000000000 --- a/spec/v2/story.spec.json +++ /dev/null @@ -1,271 +0,0 @@ -{ - "$id": "https://api.splunkresearch.com/schemas/story.json", - "$schema": "http://json-schema.org/draft-07/schema#", - "description": "The fields that make up the manifest of a version 2 Analytic Story", - "meta:license": [ - "This file is licensed to you under the Apache License, Version 2.0 (the 'License');", - "you may not use this file except in compliance with the License. You may obtain a copy", - "of the License at http://www.apache.org/licenses/LICENSE-2.0" - ], - "properties": { - "category": { - "description": "The category to which the Analytic Story belongs", - "items": { - "enum": [ - "Abuse", - "Account Compromise", - "Account Sharing", - "Adversary Tactics", - "Best Practices", - "Cloud Security", - "Command and Control", - "Compliance", - "Data Exfiltration", - "Denial of Service", - "Endpoint Compromise", - "GDPR", - "IAM Analytics", - "Insider Threat", - "Lateral Movement", - "Malware", - "Network Attack", - "Operations", - "Other", - "Privilege Escalation", - "Phishing", - "Ransomware", - "SaaS", - "Scanning", - "Shadow IT", - "Threat Intelligence", - "Unauthorized Software", - "Vulnerability", - "Web Attack" - ], - "examples": [ - "Adversary Tactics" - ], - "type": "string" - }, - "maxItems": 1, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "channel": { - "description": "A grouping function that designates where this search came from. For example, searches and stories in Enterprise Security Content Updates are in the ESCU channel", - "examples": [ - "ESCU" - ], - "type": "string" - }, - "creation_date": { - "description": "The date this story was created", - "examples": [ - "2017-11-21" - ], - "type": "string" - }, - "description": { - "description": "A high-level description or goal of the Analytic Story", - "examples": [ - "Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records." - ], - "type": "string" - }, - "detections": { - "description": "An array of detection mechanisms from Splunk, UBA and phantom. ", - "items": { - "additionalProperties": false, - "properties": { - "detection_id": { - "description": "unique identifier of the detection, in the form of UUID", - "examples": [ - "1169w17b-ef78-4b59-aae8-5369073014e1" - ], - "type": "string" - }, - "name": { - "description": "Name of the Detection. It can be a name of a Splunk correlation search name, a UBA threat or a Phantom detection playbook.", - "examples": [ - "DNS record changed" - ], - "type": "string" - }, - "type": { - "description": "What product gives you a detection", - "enum": [ - "splunk", - "uba", - "phantom" - ], - "examples": [ - "splunk", - "uba", - "phantom" - ], - "type": "string" - } - }, - "required": [ - "detection_id", - "type", - "name" - ], - "type": "object" - }, - "type": "array" - }, - "id": { - "description": "A unique identifier for the Analytic Story", - "examples": [ - "8169f17b-ef68-4b59-aae8-5869073014e1" - ], - "format": "uuid", - "type": "string" - }, - "maintainers": { - "description": "An array of the current maintainers of the Analytic Story.", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person maintaining this Analytic Story", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person maintaining this Analytic Story", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person maintaining this Analytic Story", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "modification_date": { - "description": "The date of the most recent modification to this Analytic Story", - "examples": [ - "2018-09-06" - ], - "type": "string" - }, - "name": { - "description": "The name of the Analytic Story", - "examples": [ - "DNS Hijacking" - ], - "type": "string" - }, - "narrative": { - "description": "Long-form text that describes the Analytic Story and the rationale behind it, as well as an overview of the included searches, and how they enable the story", - "examples": [ - "Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.

The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous." - ], - "type": "string" - }, - "original_authors": { - "description": "An array of the original authors of the Analytic Story", - "items": { - "additionalProperties": false, - "properties": { - "company": { - "description": "Company associated with the person who originally authored the Analytic Story", - "examples": [ - "Splunk" - ], - "type": "string" - }, - "email": { - "description": "Email address of the person who originally authored the Analytic Story", - "examples": [ - "daftpunk@splunk.com" - ], - "type": "string" - }, - "name": { - "description": "Name of the person who originally authored the Analytic Story", - "examples": [ - "Daft Punk" - ], - "type": "string" - } - }, - "required": [ - "name", - "email", - "company" - ], - "type": "object" - }, - "type": "array" - }, - "references": { - "description": "An array of URLs that give information about the problem the story is addressing", - "examples": [ - "https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html" - ], - "items": { - "type": "string" - }, - "minItems": 0, - "type": "array", - "uniqueItems": true - }, - "spec_version": { - "description": "The version of the Analytic Story specification this manifest follows", - "examples": [ - 2 - ], - "type": "integer" - }, - "usecase": { - "description": "The usecase to which the Analytic Story belongs to.", - "enum": [ - "Security Monitoring", - "Advanced Threat Detection", - "Insider Threat", - "Compliance", - "Fraud Detection" - ], - "examples": [ - "Advanced Threat Detection" - ] - }, - "version": { - "description": "The version of the Analytic Story, bump the version number when the story gets an update", - "examples": [ - "1.0" - ], - "type": "string" - } - }, - "required": [ - "id", - "category", - "usecase", - "name", - "description", - "detections", - "version" - ], - "title": "Analytic Story Manifest", - "type": "object" -} diff --git a/stories/account_monitoring.yml b/stories/account_monitoring.yml deleted file mode 100644 index cb33f90e72..0000000000 --- a/stories/account_monitoring.yml +++ /dev/null @@ -1,50 +0,0 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-08-05' -description: A common attack technique is to leverage user accounts to gain unauthorized - access to the target's network. This Analytic Story minimizes opportunities for - attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle - of system and application accounts. -detections: - - detection_id: 475b9e27-17e4-46e2-b7e2-648221be3b89 - name: Identify New User Accounts - type: splunk - - detection_id: b25f6f62-0782-43c1-b403-083231ffd97d - name: Short Lived Windows Accounts - type: splunk - - detection_id: c026e3dd-7e18-4abb-8f41-929e836efe74 - name: Detect Excessive Account Lockouts From Endpoint - type: splunk - - detection_id: 95a7f9a5-6096-437e-a19e-86f42ac609bd - name: Detect Excessive User Account Lockouts - type: splunk -id: 8892a655-6205-55f7-abba-06460e38c8ae -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-06' -name: Account Monitoring and Controls -narrative: Monitoring user accounts within your enterprise is a critical analytic - function that helps ensure that credential and access policies/procedures are properly - implemented and are being enforced. Proactive ad-hoc hunting, as well as routine - monitoring, can ensure user or system accounts are not being abused by unauthorized - individuals or processes. In the event of a network event or breach, user-authentication - logs are a key resource in determining if or how an account might have been compromised - or co-opted, leading to suspicious or malicious activity. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: - - https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' diff --git a/stories/account_monitoring_and_controls.yml b/stories/account_monitoring_and_controls.yml new file mode 100644 index 0000000000..97aa2c5e63 --- /dev/null +++ b/stories/account_monitoring_and_controls.yml @@ -0,0 +1,23 @@ +name: Account Monitoring and Controls +id: 8892a655-6205-55f7-abba-06460e38c8ae +version: 1 +date: '2017-09-06' +description: A common attack technique is to leverage user accounts to gain unauthorized + access to the target's network. This Analytic Story minimizes opportunities for + attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle + of system and application accounts. +narrative: Monitoring user accounts within your enterprise is a critical analytic + function that helps ensure that credential and access policies/procedures are properly + implemented and are being enforced. Proactive ad-hoc hunting, as well as routine + monitoring, can ensure user or system accounts are not being abused by unauthorized + individuals or processes. In the event of a network event or breach, user-authentication + logs are a key resource in determining if or how an account might have been compromised + or co-opted, leading to suspicious or malicious activity. +author: David Dorsey, Splunk +type: ESCU +references: [] +tags: + analytics_story: Account Monitoring and Controls + usecase: Security Monitoring + category: + - Best Practices diff --git a/stories/apache_struts.yml b/stories/apache_struts_vulnerability.yml similarity index 89% rename from stories/apache_struts.yml rename to stories/apache_struts_vulnerability.yml index 43e3d15724..e58c7ab44c 100644 --- a/stories/apache_struts.yml +++ b/stories/apache_struts_vulnerability.yml @@ -1,27 +1,10 @@ -category: - - Vulnerability -channel: ESCU -creation_date: '2017-03-14' +name: Apache Struts Vulnerability +id: 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e +version: 1 +date: '2018-12-06' description: Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. -detections: - - detection_id: 57a0a2bf-353f-40c1-84dc-29293f3c35b7 - name: Unusually Long Content-Type Length - type: splunk - - detection_id: ec3b7601-689a-4463-94e0-c9f45638efb9 - name: Web Servers Executing Suspicious Processes - type: splunk - - detection_id: if1fea6da-3c86-4c1d-b255-fc3b2781a491 - name: Suspicious Java Classes - type: splunk -id: 2dcfd6a2-e7d2-4873-b6ba-adaf819d2a1e -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2018-12-06' -name: Apache Struts Vulnerability narrative: 'In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months @@ -114,12 +97,12 @@ narrative: 'In March of 2017, a remote code-execution vulnerability in the Jakar process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf +tags: + analytics_story: Apache Struts Vulnerability + usecase: Advanced Threat Detection + category: + - Vulnerability diff --git a/stories/asset_tracking.yml b/stories/asset_tracking.yml index 4403bf0611..18b27b7073 100644 --- a/stories/asset_tracking.yml +++ b/stories/asset_tracking.yml @@ -1,21 +1,10 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-06-01' +name: Asset Tracking +id: 91c676cf-0b23-438d-abee-f6335e1fce77 +version: 1 +date: '2017-09-13' description: Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. -detections: - - detection_id: dcfd6b40-42f9-469d-a433-2e53f7489ff4 - name: Detect Unauthorized Assets by MAC address - type: splunk -id: 91c676cf-0b23-438d-abee-f6335e1fce77 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-13' -name: Asset Tracking narrative: This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business @@ -23,12 +12,12 @@ narrative: This Analytic Story is designed to help you develop a better understa this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/ +tags: + analytics_story: Asset Tracking + usecase: Security Monitoring + category: + - Best Practices diff --git a/stories/aws_cross_account_activity.yml b/stories/aws_cross_account_activity.yml index e50efc062c..8eec2711c4 100644 --- a/stories/aws_cross_account_activity.yml +++ b/stories/aws_cross_account_activity.yml @@ -1,21 +1,10 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-06-04' +name: AWS Cross Account Activity +id: 2f2f610a-d64d-48c2-b57c-967a2b49ab5a +version: 1 +date: '2018-06-04' description: Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. -detections: - - detection_id: 64fbbddf-fabf-4edf-80b3-0cc36ef37727 - name: AWS Cross Account Activity From Previously Unseen Account - type: splunk -id: 2f2f610a-d64d-48c2-b57c-967a2b49ab5a -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-04' -name: AWS Cross Account Activity narrative: 'Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS''s Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each @@ -36,12 +25,12 @@ narrative: 'Amazon Web Services (AWS) admins manage access to AWS resources and when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/ +tags: + analytics_story: AWS Cross Account Activity + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_cryptomining.yml b/stories/aws_cryptomining.yml index a09ca63cfd..555dc39d40 100644 --- a/stories/aws_cryptomining.yml +++ b/stories/aws_cryptomining.yml @@ -1,34 +1,11 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-03-08' +name: AWS Cryptomining +id: ced74200-8465-4bc3-bd2c-9a782eec6750 +version: 1 +date: '2018-03-08' description: Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. -detections: - - detection_id: ada0f478-84a8-4641-a3f3-d82362d6fd75 - name: EC2 Instance Started In Previously Unseen Region - type: splunk - - detection_id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4 - name: Abnormally High AWS Instances Launched by User - type: splunk - - detection_id: 65541c80-03c7-4e05-83c8-1dcd57a2e1ad - name: EC2 Instance Started With Previously Unseen Instance Type - type: splunk - - detection_id: 347ec301-601b-48b9-81aa-9ddf9c829dd3 - name: EC2 Instance Started With Previously Unseen AMI - type: splunk - - detection_id: 22773e84-bac0-4595-b086-20d3f735b4f1 - name: EC2 Instance Started With Previously Unseen User - type: splunk -id: ced74200-8465-4bc3-bd2c-9a782eec6750 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-08' -name: AWS Cryptomining narrative: 'Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it''s par for the course that ambitious, @@ -53,12 +30,12 @@ narrative: 'Cryptomining is an intentionally difficult, resource-intensive busin will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +tags: + analytics_story: AWS Cryptomining + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_ec2.yml b/stories/aws_ec2.yml deleted file mode 100644 index f6c1a56022..0000000000 --- a/stories/aws_ec2.yml +++ /dev/null @@ -1,51 +0,0 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-02-09' -description: Use the searches in this Analytic Story to monitor your AWS EC2 instances - for evidence of anomalous activity and suspicious behaviors, such as EC2 instances - that originate from unusual locations or those launched by previously unseen users - (among others). Included investigative searches will help you probe more deeply, - when the information warrants it. -detections: - - detection_id: ada0f478-84a8-4641-a3f3-d82362d6fd75 - name: EC2 Instance Started In Previously Unseen Region - type: splunk - - detection_id: ada0f478-84a8-4641-s3f3-d82362dffd75 - name: Abnormally High AWS Instances Terminated by User - type: splunk - - detection_id: 2a9b80d3-6340-4345-b5ad-290bf5d0dac4 - name: Abnormally High AWS Instances Launched by User - type: splunk - - detection_id: 22773e84-bac0-4595-b086-20d3f735b4f1 - name: EC2 Instance Started With Previously Unseen User - type: splunk - - detection_id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5 - name: Abnormally High AWS Instances Launched by User - MLTK - type: splunk - - detection_id: 1c02b86a-cd85-473e-a50b-014a9ac8fe3e - name: Abnormally High AWS Instances Terminated by User - MLTK - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50f1268af3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-02-09' -name: Suspicious AWS EC2 Activities -narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance, - and risk auditing within your AWS account. Actions taken by a user, role, or an - AWS service are recorded as events in CloudTrail. It is crucial for a company to - monitor events and actions taken in the AWS Console, AWS command-line interface, - and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. - This Analytic Story identifies suspicious activities in your AWS EC2 instances and - helps you respond and investigate those activities. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' diff --git a/stories/aws_network_acl.yml b/stories/aws_network_acl.yml deleted file mode 100644 index 37eba11cb6..0000000000 --- a/stories/aws_network_acl.yml +++ /dev/null @@ -1,44 +0,0 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-01-10' -description: Monitor your AWS network infrastructure for bad configurations and malicious - activity. Investigative searches help you probe deeper, when the facts warrant it. -detections: - - detection_id: ada0f478-84a8-4641-a3f1-d82362d6bd75 - name: AWS Network Access Control List Created with All Open Ports - type: splunk - - detection_id: ada0f478-84a8-4641-a3f1-d82362d6fd75 - name: AWS Network Access Control List Deleted - type: splunk - - detection_id: ada0f278-84a8-46w1-a3f1-w32372d4bd53 - name: Detect Spike in blocked Outbound Traffic from your AWS - type: splunk - - detection_id: ada0f478-84a8-4641-a1f1-e32372d4bd53 - name: Detect Spike in Network ACL Activity - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50ff268af4 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-21' -name: AWS Network ACL Activity -narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance, - and operational/risk auditing of your AWS account. Actions taken by a user, role, - or an AWS service are recorded as events in CloudTrail. It is crucial for a company - to monitor events and actions taken in the AWS Management Console, AWS Command Line - Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable - to attacks. This analytic story contains detection searches that leverage CloudTrail - logs from AWS to check for bad configurations and malicious activity in your AWS - network access controls. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html - - https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ -spec_version: 2 -usecase: Security Monitoring -version: '2.0' diff --git a/stories/aws_network_acl_activity.yml b/stories/aws_network_acl_activity.yml new file mode 100644 index 0000000000..642741a086 --- /dev/null +++ b/stories/aws_network_acl_activity.yml @@ -0,0 +1,24 @@ +name: AWS Network ACL Activity +id: 2e8948a5-5239-406b-b56b-6c50ff268af4 +version: 2 +date: '2018-05-21' +description: Monitor your AWS network infrastructure for bad configurations and malicious + activity. Investigative searches help you probe deeper, when the facts warrant it. +narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance, + and operational/risk auditing of your AWS account. Actions taken by a user, role, + or an AWS service are recorded as events in CloudTrail. It is crucial for a company + to monitor events and actions taken in the AWS Management Console, AWS Command Line + Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable + to attacks. This analytic story contains detection searches that leverage CloudTrail + logs from AWS to check for bad configurations and malicious activity in your AWS + network access controls. +author: Bhavin Patel, Splunk +type: ESCU +references: +- https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html +- https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/ +tags: + analytics_story: AWS Network ACL Activity + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_suspcious_logins.yml b/stories/aws_suspcious_logins.yml deleted file mode 100644 index 4dce0bcb9e..0000000000 --- a/stories/aws_suspcious_logins.yml +++ /dev/null @@ -1,44 +0,0 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-02-24' -description: 'Monitor your AWS authentication events using your CloudTrail logs. Searches - within this Analytic Story will help you stay aware of and investigate suspicious - logins. ' -detections: - - detection_id: ada0f478-84a8-4641-a3f3-d82362dffd75 - name: Detect new user AWS Console Login - type: splunk - - detection_id: 121b0b11-f8ac-4ed6-a132-3800ca4fc07a - name: Detect AWS Console Login by User from New City - type: splunk - - detection_id: 9f31aa8e-e37c-46bc-bce1-8b3be646d026 - name: Detect AWS Console Login by User from New Region - type: splunk - - detection_id: 67bd3def-c41c-4bf6-837b-ae196b4257c6 - name: Detect AWS Console Login by User from New Country - type: splunk -id: 2e8948a5-5239-406b-b56b-6c59f1268af3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel - - company: Splunk - email: jbrewer@splunk.com - name: Jason Brewer -modification_date: '2019-05-01' -name: Suspicious AWS Login Activities -narrative: It is important to monitor and control who has access to your AWS infrastructure. - Detecting suspicious logins to your AWS infrastructure will provide good starting - points for investigations. Abusive behaviors caused by compromised credentials can - lead to direct monetary costs, as you will be billed for any EC2 instances created - by the attacker. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html -spec_version: 2 -usecase: Security Monitoring -version: '1.0' diff --git a/stories/aws_provisioning.yml b/stories/aws_suspicious_provisioning_activities.yml similarity index 59% rename from stories/aws_provisioning.yml rename to stories/aws_suspicious_provisioning_activities.yml index dc06495b73..06d443a817 100644 --- a/stories/aws_provisioning.yml +++ b/stories/aws_suspicious_provisioning_activities.yml @@ -1,30 +1,10 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-03-16' +name: AWS Suspicious Provisioning Activities +id: 3338b567-3804-4261-9889-cf0ca4753c7f +version: 1 +date: '2018-03-16' description: Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. -detections: - - detection_id: ceb8d3d8-06cb-49eb-beaf-829526e33ff0 - name: AWS Cloud Provisioning From Previously Unseen Country - type: splunk - - detection_id: 7971d3df-da82-4648-a6e5-b5637bea5253 - name: AWS Cloud Provisioning From Previously Unseen Region - type: splunk - - detection_id: 344a1778-0b25-490c-adb1-de8beddf59cd - name: AWS Cloud Provisioning From Previously Unseen City - type: splunk - - detection_id: 42e15012-ac14-4801-94f4-f1acbe64880b - name: AWS Cloud Provisioning From Previously Unseen IP Address - type: splunk -id: 3338b567-3804-4261-9889-cf0ca4753c7f -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-03-16' -name: AWS Suspicious Provisioning Activities narrative: 'Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where @@ -39,12 +19,12 @@ narrative: 'Because most enterprise AWS activities originate from familiar geogr reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +tags: + analytics_story: AWS Suspicious Provisioning Activities + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_user_monitoring.yml b/stories/aws_user_monitoring.yml index b308aa7d2a..af43bd792e 100644 --- a/stories/aws_user_monitoring.yml +++ b/stories/aws_user_monitoring.yml @@ -1,33 +1,10 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-03-12' +name: AWS User Monitoring +id: 2e8948a5-5239-406b-b56b-6c50f1269af3 +version: 1 +date: '2018-03-12' description: Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. -detections: - - detection_id: ada0f478-84a8-4641-a3f1-d82362d4bd55 - name: Detect AWS API Activities From Unapproved Accounts - type: splunk - - detection_id: ada0f478-84a8-4641-a3f1-d32362d4bd55 - name: Detect Spike in AWS API Activity - type: splunk - - detection_id: 22773e84-bac0-4595-b086-20d3f335b4f1 - name: Detect new API calls from user roles - type: splunk - - detection_id: ada0f478-84a8-4641-a3f1-e32372d4bd53 - name: Detect Spike in Security Group Activity - type: splunk - - detection_id: 2a9b80d3-6340-4345-w5ad-212bf5d1dac4 - name: Detect API activity from users without MFA - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50f1269af3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-03-12' -name: AWS User Monitoring narrative: 'It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it''s all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable @@ -48,13 +25,13 @@ narrative: 'It seems obvious that it is critical to monitor and control the user The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - - https://redlock.io/blog/cryptojacking-tesla -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +- https://redlock.io/blog/cryptojacking-tesla +tags: + analytics_story: AWS User Monitoring + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/brand_monitoring.yml b/stories/brand_monitoring.yml index fdef9aea8f..dd27bee03f 100644 --- a/stories/brand_monitoring.yml +++ b/stories/brand_monitoring.yml @@ -1,27 +1,10 @@ -category: - - Abuse -channel: ESCU -creation_date: '2017-06-01' +name: Brand Monitoring +id: 91c676cf-0b23-438d-abee-f6335e1fce78 +version: 1 +date: '2017-12-19' description: Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. -detections: - - detection_id: 24dd17b1-e2fb-4c31-878c-d4f746595bfa - name: Monitor DNS For Brand Abuse - type: splunk - - detection_id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 - name: Monitor Email For Brand Abuse - type: splunk - - detection_id: 134da869-e264-4a8f-8d7e-fcd0ec88f301 - name: Monitor Web Traffic For Brand Abuse - type: splunk -id: 91c676cf-0b23-438d-abee-f6335e1fce78 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-12-19' -name: Brand Monitoring narrative: 'While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and @@ -40,14 +23,14 @@ narrative: 'While you can educate your users and customers about the risks and t provide you with even more actionable intelligence, including likely geographic information, contextual searches to help you scope the problem, and investigative searches.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ - - https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ - - https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.zerofox.com/blog/what-is-digital-risk-monitoring/ +- https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/ +- https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/ +tags: + analytics_story: Brand Monitoring + usecase: Advanced Threat Detection + category: + - Abuse diff --git a/stories/cloud_cryptomining.yml b/stories/cloud_cryptomining.yml index d73312b61c..4341815a17 100644 --- a/stories/cloud_cryptomining.yml +++ b/stories/cloud_cryptomining.yml @@ -1,34 +1,11 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2019-10-02' +name: Cloud Cryptomining +id: 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a +version: 1 +date: '2019-10-02' description: Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. -detections: - - detection_id: fa4089e2-50e3-40f7-8469-d2cc1564ca59 - name: Cloud Compute Instance Started In Previously Unused Region - type: splunk - - detection_id: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda - name: Cloud Compute Instance Created With Previously Unseen Instance Type - type: splunk - - detection_id: bc24922d-987c-4645-b288-f8c73ec194c4 - name: Cloud Compute Instance Created With Previously Unseen Image - type: splunk - - detection_id: 76988f6a-3935-48f6-a9e5-6fca8b3ed843 - name: Cloud Compute Instance Created By Previously Unseen User - type: splunk - - detection_id: dec41ad5-d579-42cb-b4c6-f5dbb778bbe5 - name: Abnormally High AWS Instances Launched by User - MLTK - type: splunk -id: 3b96d13c-fdc7-45dd-b3ad-c132b31cdd2a -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2019-10-02' -name: Cloud Cryptomining narrative: 'Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it''s par for the course that ambitious, @@ -44,21 +21,21 @@ narrative: 'Cryptomining is an intentionally difficult, resource-intensive busin usage, and hiding the mining pool''s IP address behind a free CDN. \ When malicious miners appropriate a cloud instance, often spinning up hundreds of - new instances, the costs can become astronomical for the account holder. So it - is critically important to monitor your systems for suspicious activities that could + new instances, the costs can become astronomical for the account holder. So it is + critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ This Analytic Story is focused on detecting suspicious new instances in your cloud - environment to help prevent cryptominers from gaining a foothold. It contains detection searches that - will detect when a previously unused instance type or AMI is used. It also contains - support searches to build lookup files to ensure proper execution of the detection - searches.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey + environment to help prevent cryptominers from gaining a foothold. It contains detection + searches that will detect when a previously unused instance type or AMI is used. + It also contains support searches to build lookup files to ensure proper execution + of the detection searches.' +author: David Dorsey, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +tags: + analytics_story: Cloud Cryptomining + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/coldroot.yml b/stories/coldroot_macos_rat.yml similarity index 73% rename from stories/coldroot.yml rename to stories/coldroot_macos_rat.yml index c205c366b1..a9ca577bd2 100644 --- a/stories/coldroot.yml +++ b/stories/coldroot_macos_rat.yml @@ -1,26 +1,12 @@ -category: - - Malware -channel: ESCU -creation_date: '2019-01-29' +name: ColdRoot MacOS RAT +id: bd91a2bc-d20b-4f44-a982-1bea98e86390 +version: 1 +date: '2019-01-09' description: Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. -detections: - - detection_id: 2a371608-331d-4034-ae2c-21dda8f1d0ec - name: Processes Tapping Keyboard Events - type: splunk - - detection_id: a6fffe5e-05c3-4c04-badc-887607fbb8dc - name: Osquery pack - ColdRoot detection - type: splunk -id: bd91a2bc-d20b-4f44-a982-1bea98e86390 -maintainers: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez -modification_date: '2019-01-09' -name: ColdRoot MacOS RAT narrative: 'Conventional wisdom holds that Apple''s MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this @@ -45,14 +31,14 @@ narrative: 'Conventional wisdom holds that Apple''s MacOS operating system is si Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger.' -original_authors: - - company: Splunk - email: jhernandez@splunk.com - name: Jose Hernandez +author: Jose Hernandez, Splunk +type: ESCU references: - - https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ - - https://objective-see.com/blog/blog_0x2A.html - - https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/ +- https://objective-see.com/blog/blog_0x2A.html +- https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/ +tags: + analytics_story: ColdRoot MacOS RAT + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/collection_and_staging.yml b/stories/collection_and_staging.yml index adaf727b97..81ba716e0f 100644 --- a/stories/collection_and_staging.yml +++ b/stories/collection_and_staging.yml @@ -1,34 +1,11 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-01-08' +name: Collection and Staging +id: 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a +version: 1 +date: '2020-02-03' description: 'Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. ' -detections: - - detection_id: b5541828-8ffd-4070-9d95-b3da4de924cb - name: Suspicious writes to windows Recycle Bin - type: splunk - - detection_id: cd6297cd-2bdd-4aa1-84aa-5d2f84228fac - name: Suspicious writes to System Volume Information - type: splunk - - detection_id: ee18ed37-0802-4268-9435-b3b91aaa18xx - name: Email files written outside of the Outlook directory - type: splunk - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b556368 - name: Hosts receiving high volume of network traffic from email server - type: splunk - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b556378 - name: Email servers sending high volume traffic to hosts - type: splunk -id: 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-02-03' -name: Collection and Staging narrative: "A common adversary goal is to identify and exfiltrate data of value from\ \ a target organization. This data may include email conversations and addresses,\ \ confidential company information, links to network design/infrastructure, important\ @@ -40,13 +17,13 @@ narrative: "A common adversary goal is to identify and exfiltrate data of value \ encrypting) it. All of these activities provide opportunities for defenders to\ \ identify their presence. \\\nUse the searches to detect and monitor suspicious\ \ behavior related to these activities." -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://attack.mitre.org/wiki/Collection - - https://attack.mitre.org/wiki/Technique/T1074 -spec_version: 2 -usecase: Security Monitoring -version: '1.1' +- https://attack.mitre.org/wiki/Collection +- https://attack.mitre.org/wiki/Technique/T1074 +tags: + analytics_story: Collection and Staging + usecase: Security Monitoring + category: + - Adversary Tactics diff --git a/stories/command_and_control.yml b/stories/command_and_control.yml index c85f5c7969..88b1ff4876 100644 --- a/stories/command_and_control.yml +++ b/stories/command_and_control.yml @@ -1,58 +1,11 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-06-01' +name: Command and Control +id: 943773c6-c4de-4f38-89a8-0b92f98804d8 +version: 1 +date: '2018-06-01' description: Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. -detections: - - detection_id: e9c102de-4d43-42a7-b1c8-8062ea297419 - name: Detect Large Outbound ICMP Packets - type: splunk - - detection_id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3 - name: Protocol or Port Mismatch - type: splunk - - detection_id: 104658f4-afdc-499f-9719-17a43f9826f4 - name: Detection of DNS Tunnels - type: splunk - - detection_id: ea688274-9c06-4473-b951-e4cb7a5d7a45 - name: TOR Traffic - type: splunk - - detection_id: ce5a0962-849f-4720-a678-753fe6674479 - name: Prohibited Network Traffic Allowed - type: splunk - - detection_id: 74ec6f18-604b-4202-a567-86b2066be3ce - name: Clients Connecting to Multiple DNS Servers - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 - name: DNS Query Length With High Standard Deviation - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk - - detection_id: 104658f4-afdc-499e-9719-17243f9826f1 - name: Excessive DNS Failures - type: splunk - - detection_id: 05437c07-62f5-452e-afdc-04dd44815bb9 - name: Detect Long DNS TXT Record Response - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 - name: DNS Query Requests Resolved by Unauthorized DNS Servers - type: splunk - - detection_id: ada0f278-84a8-46w1-a3f1-w32372d4bd53 - name: Detect Spike in blocked Outbound Traffic from your AWS - type: splunk - - detection_id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 - name: DNS Query Length Outliers - MLTK - type: splunk -id: 943773c6-c4de-4f38-89a8-0b92f98804d8 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2018-06-01' -name: Command and Control narrative: 'Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. @@ -69,13 +22,13 @@ narrative: 'Threat actors typically architect and implement an infrastructure to Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://attack.mitre.org/wiki/Command_and_Control - - https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://attack.mitre.org/wiki/Command_and_Control +- https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware +tags: + analytics_story: Command and Control + usecase: Security Monitoring + category: + - Adversary Tactics diff --git a/stories/common_phishing_frameworks.yml b/stories/common_phishing_frameworks.yml index 87ff688f7c..4f6f64bd4b 100644 --- a/stories/common_phishing_frameworks.yml +++ b/stories/common_phishing_frameworks.yml @@ -1,21 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2019-04-29' +name: Common Phishing Frameworks +id: 9a64ab44-9214-4639-8163-7eaa2621bd61 +version: 1 +date: '2019-04-29' description: 'Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. ' -detections: - - detection_id: 24dd17b1-e2fb-4c31-878c-d4f226595bfa - name: Detect DNS requests to Phishing Sites leveraging EvilGinx2 - type: splunk -id: 9a64ab44-9214-4639-8163-7eaa2621bd61 -maintainers: - - company: Splunk - email: research@splunk.com - name: Splunk Research Team -modification_date: '2019-04-29' -name: Common Phishing Frameworks narrative: 'As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), @@ -33,14 +22,14 @@ narrative: 'As most people know, these emails use fraudulent domains, [email scr Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2.' -original_authors: - - company: Splunk - email: research@splunk.com - name: Splunk Research Team +author: Splunk Research Team, Splunk +type: ESCU references: - - https://github.com/kgretzky/evilginx2 - - https://attack.mitre.org/techniques/T1192/ - - https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://github.com/kgretzky/evilginx2 +- https://attack.mitre.org/techniques/T1192/ +- https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/ +tags: + analytics_story: Common Phishing Frameworks + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/container_implant_story.yml b/stories/container_implant_story.yml deleted file mode 100644 index 55bd8dca69..0000000000 --- a/stories/container_implant_story.yml +++ /dev/null @@ -1,38 +0,0 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2020-02-20' -description: Use the searches in this story to monitor your Kubernetes registry repositories - for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These - searches provide information on source users, destination path, container names and repository - names. The searches provide context to address Mitre T1525 which refers to container implantation - upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry - and Azure Container Registry. -detections: - - detection_id: f0f70b40-f7ad-489d-9905-23d149da8099 - name: New container uploaded to AWS ECR - type: splunk - - detection_id: 4f00ca88-e766-4605-ac65-ae51c9fd185b - name: GCP GCR container uploaded - type: splunk -id: aa0e28b1-0521-4b6f-9d2a-7b87e34af246 -maintainers: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -name: Container Implantation Monitoring & Investigation -narrative: Container Registrys provide a way for organizations to keep customized images of their - development and infrastructure environment in private. However if these repositories are misconfigured - or priviledge users credentials are compromise, attackers can potentially upload implanted containers - which can be deployed across the organization. These searches allow operator to monitor who, when and what - was uploaded to container registry. -original_authors: - - company: Splunk - email: rsoto@splunk.com, rvaldez@splunk.com - name: Rod Soto, Rico Valdez -spec_version: 2 -references: [] -usecase: Security Monitoring -version: '1.0' -references: - - https://github.com/splunk/cloud-datamodel-security-research diff --git a/stories/container_implantation_monitoring_and_investigation.yml b/stories/container_implantation_monitoring_and_investigation.yml new file mode 100644 index 0000000000..66db77b1f8 --- /dev/null +++ b/stories/container_implantation_monitoring_and_investigation.yml @@ -0,0 +1,26 @@ +name: Container Implantation Monitoring and Investigation +id: aa0e28b1-0521-4b6f-9d2a-7b87e34af246 +version: 1 +date: '2020-02-20' +description: Use the searches in this story to monitor your Kubernetes registry repositories + for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. + These searches provide information on source users, destination path, container + names and repository names. The searches provide context to address Mitre T1525 + which refers to container implantation upload to a company's repository either in + Amazon Elastic Container Registry, Google Container Registry and Azure Container + Registry. +narrative: Container Registrys provide a way for organizations to keep customized + images of their development and infrastructure environment in private. However if + these repositories are misconfigured or priviledge users credentials are compromise, + attackers can potentially upload implanted containers which can be deployed across + the organization. These searches allow operator to monitor who, when and what was + uploaded to container registry. +author: Rod Soto, Rico Valdez, Splunk +type: ESCU +references: +- https://github.com/splunk/cloud-datamodel-security-research +tags: + analytics_story: Container Implantation Monitoring and Investigation + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/credential_dumping.yml b/stories/credential_dumping.yml index e921034795..5f20c92524 100644 --- a/stories/credential_dumping.yml +++ b/stories/credential_dumping.yml @@ -1,59 +1,12 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-08-08' +name: Credential Dumping +id: 854d78bf-d0e2-4f4e-b05c-640905f86d7a +version: 3 +date: '2020-02-04' description: Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. -detections: - - detection_id: fb4c31b0-13e8-4155-8aa5-24de4b8d6717 - name: Access LSASS Memory for Dump Creation - type: splunk - - detection_id: 67d4dbef-9564-4699-8da8-03a151529edc - name: Create Remote Thread into LSASS - type: splunk - - detection_id: 2c365e57-4414-4540-8dc0-73ab10729996 - name: Detect Credential Dumping through LSASS access - type: splunk - - detection_id: 56ef054c-76ef-45f9-af4a-a634695dcd65 - name: Unsigned Image Loaded by LSASS - type: splunk - - detection_id: 14038953-e5f2-4daf-acff-5452062baf03 - name: Attempted Credential Dump From Registry via Reg.exe - type: splunk - - detection_id: 29e307ba-40af-4ab2-91b2-3c6b392bbba0 - name: Detect Mimikatz Using Loaded Images - type: splunk - - detection_id: c2590137-0b08-4985-9ec5-6ae23d92f63d - name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - type: splunk - - detection_id: eb120f5f-b879-4a63-97c1-93352b5df844 - name: Creation of Shadow Copy - type: splunk - - detection_id: 2ed8b538-d284-449a-be1d-82ad1dbd186b - name: Creation of Shadow Copy with wmic and powershell - type: splunk - - detection_id: d8c406fe-23d2-45f3-a983-1abe7b83ff3b - name: Credential Dumping via Copy Command from Shadowcopy - type: splunk - - detection_id: c5eac648-fae0-4263-91a6-773df1f4c903 - name: Credential Dumping via Symlink to Shadowcopy - type: splunk - - detection_id: 8943b567-f14d-4ee8-a0bb-2121d4ce3184 - name: Dump LSASS via comsvcs DLL - type: splunk -id: 854d78bf-d0e2-4f4e-b05c-640905f86d7a -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez - - company: Splunk - email: pbareiss@splunk.com - name: Patrick Bareiss -modification_date: '2020-02-04' -name: Credential Dumping narrative: 'Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking @@ -69,13 +22,13 @@ narrative: 'Credential dumping—gathering credentials from a target system, The detection searches in this Analytic Story monitor access to the Local Security Authority Subsystem Service (LSASS) process, the usage of shadowcopies for credential dumping and some other techniques for credential dumping.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://attack.mitre.org/wiki/Technique/T1003 - - https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '3.0' +- https://attack.mitre.org/wiki/Technique/T1003 +- https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html +tags: + analytics_story: Credential Dumping + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/data_protection.yml b/stories/data_protection.yml index 196479aedd..36e5f876af 100644 --- a/stories/data_protection.yml +++ b/stories/data_protection.yml @@ -1,41 +1,24 @@ -category: - - Abuse -channel: ESCU -creation_date: '2017-06-01' +name: Data Protection +id: 91c676cf-0b23-438d-abee-f6335e1fce33 +version: 1 +date: '2017-09-14' description: Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. -detections: - - detection_id: 104658f4-afdc-499f-9719-17a43f9826f4 - name: Detection of DNS Tunnels - type: splunk - - detection_id: 104658f4-afdc-499f-9719-17a43f9826f5 - name: Detect USB device insertion - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk -id: 91c676cf-0b23-438d-abee-f6335e1fce33 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-14' -name: Data Protection narrative: Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.cisecurity.org/controls/data-protection/ - - https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 - - https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://www.cisecurity.org/controls/data-protection/ +- https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022 +- https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ +tags: + analytics_story: Data Protection + usecase: Security Monitoring + category: + - Abuse diff --git a/stories/defense_evasion.yml b/stories/defense_evasion.yml deleted file mode 100644 index f05a1cb486..0000000000 --- a/stories/defense_evasion.yml +++ /dev/null @@ -1,44 +0,0 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-10-11' -description: 'Detect tactics used by malware to evade defenses on Windows endpoints. - A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` - and disabling user-account control, among many others ' -detections: - - detection_id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 - name: Suspicious Reg.exe Process - type: splunk - - detection_id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c - name: Disabling Remote User Account Control - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6b5264g9f - name: Hiding Files And Directories With Attrib.exe - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6b5264x9f - name: Reg.exe used to hide files/directories via registry keys - type: splunk - - detection_id: c9f4b923-f8af-4155-b697-1354f5dcbc5e - name: Remote Registry Key modifications - type: splunk -id: 56e24a28-5003-4047-b2db-e8f3c4618064 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-05-31' -name: Windows Defense Evasion Tactics -narrative: Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that - adversaries employ in a variety of ways to bypass or defeat defensive security measures. - There are many techniques enumerated by the MITRE ATT&CK framework that are applicable - in this context. This Analytic Story includes searches designed to identify the - use of such techniques on Windows platforms. -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -references: - - https://attack.mitre.org/wiki/Defense_Evasion -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' diff --git a/stories/dhs_report_TA18-074A.yml b/stories/dhs_report_TA18-074A.yml deleted file mode 100644 index 7d2b3215da..0000000000 --- a/stories/dhs_report_TA18-074A.yml +++ /dev/null @@ -1,88 +0,0 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-03-19' -description: Monitor for suspicious activities associated with DHS Technical Alert - US-CERT TA18-074A. Some of the activities that adversaries used in these compromises - included spearfishing attacks, malware, watering-hole domains, many and more. -detections: - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b936378 - name: SMB Traffic Spike - type: splunk - - detection_id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 - name: SMB Traffic Spike - MLTK - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-95dbb162040e - name: Processes launching netsh - type: splunk - - detection_id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 - name: Suspicious Reg.exe Process - type: splunk - - detection_id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d - name: Sc.exe Manipulating Windows Services - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-151bb162040e - name: Create local admin accounts using net.exe - type: splunk - - detection_id: a4214f0b-e01c-41bc-8cc4-d2b71e3056b4 - name: Single Letter Process On Endpoint - type: splunk - - detection_id: d5af132c-7c17-439c-9d31-13d55340f36c - name: Scheduled Task Name Used by Dragonfly Threat Actors - type: splunk - - detection_id: 9be56c82-b1cc-4318-87eb-d138afaaca39 - name: Malicious PowerShell Process - Execution Policy Bypass - type: splunk - - detection_id: 7f5fb3e1-4209-414-90db-0ec21b936378 - name: Detect Outbound SMB Traffic - type: splunk - - detection_id: b25f6f62-0712-43c1-b203-083231ffd97d - name: Detect New Local Admin account - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-151xb162040e - name: Detect PsExec With accepteula Flag - type: splunk - - detection_id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 - name: First time seen command line argument - type: splunk -id: 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-01-22' -name: DHS Report TA18-074A -narrative: 'The frequency of nation-state cyber attacks has increased significantly - over the last decade. Employing numerous tactics and techniques, these attacks continue - to escalate in complexity. \ - - There is a wide range of motivations for these state-sponsored hacks, including - stealing valuable corporate, military, or diplomatic dataѿall of which could - confer advantages in various arenas. They may also target critical infrastructure. - \ - - One joint Technical Alert (TA) issued by the Department of Homeland and the FBI - in mid-March of 2018 attributed some cyber activity targeting utility infrastructure - to operatives sponsored by the Russian government. The hackers executed spearfishing - attacks, installed malware, employed watering-hole domains, and more. While they - caused no physical damage, the attacks provoked fears that a nation-state could - turn off water, redirect power, or compromise a nuclear power plant.\ - - Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify - the network configuration), suspicious registry modifications, and many more--may - all be events you may wish to investigate further. While the use of these technique - may be an indication that a nation-state actor is attempting to compromise your - environment, it is important to note that these techniques are often employed by - other groups, as well.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: - - https://www.us-cert.gov/ncas/alerts/TA18-074A -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' diff --git a/stories/dhs_report_ta18_074a.yml b/stories/dhs_report_ta18_074a.yml new file mode 100644 index 0000000000..2104110345 --- /dev/null +++ b/stories/dhs_report_ta18_074a.yml @@ -0,0 +1,38 @@ +name: DHS Report TA18-074A +id: 0c016e5c-88be-4e2c-8c6c-c2b55b4fb4ef +version: 2 +date: '2020-01-22' +description: Monitor for suspicious activities associated with DHS Technical Alert + US-CERT TA18-074A. Some of the activities that adversaries used in these compromises + included spearfishing attacks, malware, watering-hole domains, many and more. +narrative: 'The frequency of nation-state cyber attacks has increased significantly + over the last decade. Employing numerous tactics and techniques, these attacks continue + to escalate in complexity. \ + + There is a wide range of motivations for these state-sponsored hacks, including + stealing valuable corporate, military, or diplomatic dataѿall of which could + confer advantages in various arenas. They may also target critical infrastructure. + \ + + One joint Technical Alert (TA) issued by the Department of Homeland and the FBI + in mid-March of 2018 attributed some cyber activity targeting utility infrastructure + to operatives sponsored by the Russian government. The hackers executed spearfishing + attacks, installed malware, employed watering-hole domains, and more. While they + caused no physical damage, the attacks provoked fears that a nation-state could + turn off water, redirect power, or compromise a nuclear power plant.\ + + Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify + the network configuration), suspicious registry modifications, and many more--may + all be events you may wish to investigate further. While the use of these technique + may be an indication that a nation-state actor is attempting to compromise your + environment, it is important to note that these techniques are often employed by + other groups, as well.' +author: Rico Valdez, Splunk +type: ESCU +references: +- https://www.us-cert.gov/ncas/alerts/TA18-074A +tags: + analytics_story: DHS Report TA18-074A + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/disable_security_tools.yml b/stories/disable_security_tools.yml deleted file mode 100644 index 3e5f59e9de..0000000000 --- a/stories/disable_security_tools.yml +++ /dev/null @@ -1,54 +0,0 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-04-09' -description: Looks for activities and techniques associated with the disabling of - security tools on a Windows system, such as suspicious `reg.exe` processes, processes - launching netsh, and many others. -detections: - - detection_id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d - name: Sc.exe Manipulating Windows Services - type: splunk - - detection_id: a6b3ab4e-dd77-4213-95fa-fc94701995e0 - name: Suspicious Reg.exe Process - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-95dbb162040e - name: Processes launching netsh - type: splunk - - detection_id: c8e349c6-b97c-486e-8949-bd7bcd1f3910 - name: Attempt To Stop Security Service - type: splunk - - detection_id: 6bc5243e-ef36-45dc-9b12-f4a6be131159 - name: Attempt To Add Certificate To Untrusted Store - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f665664g9f - name: Unload Sysmon Filter Driver - type: splunk -id: fcc27099-46a0-46b0-a271-5c7dab56b6f1 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-02-04' -name: Disabling Security Tools -narrative: Attackers employ a variety of tactics in order to avoid detection and operate - without barriers. This often involves modifying the configuration of security tools - to get around them or explicitly disabling them to prevent them from running. This - Analytic Story includes searches that look for activity consistent with attackers - attempting to disable various security mechanisms. Such activity may involve monitoring - for suspicious registry activity, as this is where much of the configuration for - Windows and various other programs reside, or explicitly attempting to shut down - security-related services. Other times, attackers attempt various tricks to prevent - specific programs from running, such as adding the certificates with which the security - tools are signed to a blacklist (which would prevent them from running). -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: - - https://attack.mitre.org/wiki/Technique/T1089 - - https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ - - https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf -spec_version: 2 -usecase: Security Monitoring -version: '2.0' diff --git a/stories/disabling_security_tools.yml b/stories/disabling_security_tools.yml new file mode 100644 index 0000000000..12cf57def9 --- /dev/null +++ b/stories/disabling_security_tools.yml @@ -0,0 +1,28 @@ +name: Disabling Security Tools +id: fcc27099-46a0-46b0-a271-5c7dab56b6f1 +version: 2 +date: '2020-02-04' +description: Looks for activities and techniques associated with the disabling of + security tools on a Windows system, such as suspicious `reg.exe` processes, processes + launching netsh, and many others. +narrative: Attackers employ a variety of tactics in order to avoid detection and operate + without barriers. This often involves modifying the configuration of security tools + to get around them or explicitly disabling them to prevent them from running. This + Analytic Story includes searches that look for activity consistent with attackers + attempting to disable various security mechanisms. Such activity may involve monitoring + for suspicious registry activity, as this is where much of the configuration for + Windows and various other programs reside, or explicitly attempting to shut down + security-related services. Other times, attackers attempt various tricks to prevent + specific programs from running, such as adding the certificates with which the security + tools are signed to a blacklist (which would prevent them from running). +author: Rico Valdez, Splunk +type: ESCU +references: +- https://attack.mitre.org/wiki/Technique/T1089 +- https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/ +- https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf +tags: + analytics_story: Disabling Security Tools + usecase: Security Monitoring + category: + - Adversary Tactics diff --git a/stories/dns_amplification_attacks.yml b/stories/dns_amplification_attacks.yml index d9a22839d2..d273831862 100644 --- a/stories/dns_amplification_attacks.yml +++ b/stories/dns_amplification_attacks.yml @@ -1,22 +1,11 @@ -category: - - Abuse -channel: ESCU -creation_date: '2016-08-24' +name: DNS Amplification Attacks +id: e8afd39e-3294-11e6-b39d-a45e60c6700 +version: 1 +date: '2016-09-13' description: DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. -detections: - - detection_id: 8fa891f7-a533-4b3c-af85-5aa2e7c1f1eb - name: Large Volume of DNS ANY Queries - type: splunk -id: e8afd39e-3294-11e6-b39d-a45e60c6700 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2016-09-13' -name: DNS Amplification Attacks narrative: 'The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification @@ -34,13 +23,13 @@ narrative: 'The Domain Name System (DNS) is the protocol used to map domain name The search in this story can help you to detect if attackers are abusing your company''s DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.us-cert.gov/ncas/alerts/TA13-088A - - https://www.imperva.com/learn/application-security/dns-amplification/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://www.us-cert.gov/ncas/alerts/TA13-088A +- https://www.imperva.com/learn/application-security/dns-amplification/ +tags: + analytics_story: DNS Amplification Attacks + usecase: Security Monitoring + category: + - Abuse diff --git a/stories/dns_hijacking.yml b/stories/dns_hijacking.yml index 71e12483fe..0ff67cdfda 100644 --- a/stories/dns_hijacking.yml +++ b/stories/dns_hijacking.yml @@ -1,29 +1,9 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-11-21' +name: DNS Hijacking +id: 8169f17b-ef68-4b59-aa28-586907301221 +version: 1 +date: '2020-02-04' description: Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. -detections: - - detection_id: 44d3a43e-dcd5-49f7-8356-5209bb369065 - name: DNS record changed - type: splunk - - detection_id: 74ec6f18-604b-4202-a567-86b2066be3ce - name: Clients Connecting to Multiple DNS Servers - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 - name: DNS Query Requests Resolved by Unauthorized DNS Servers - type: splunk -id: 8169f17b-ef68-4b59-aa28-586907301221 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-02-04' -name: DNS Hijacking narrative: 'Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections @@ -73,15 +53,15 @@ narrative: 'Dubbed the Achilles heel of the Internet (see https://www.f5.com/lab The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html - - https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ - - http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ - - https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html +- https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ +- http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ +- https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html +tags: + analytics_story: DNS Hijacking + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/dynamic_dns.yml b/stories/dynamic_dns.yml index 0042ed1e9c..3ebcea30c9 100644 --- a/stories/dynamic_dns.yml +++ b/stories/dynamic_dns.yml @@ -1,24 +1,10 @@ -category: - - Malware -channel: ESCU -creation_date: '2017-11-21' +name: Dynamic DNS +id: 8169f17b-ef68-4b59-aae8-586907301221 +version: 2 +date: '2018-09-06' description: Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. -detections: - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk - - detection_id: 134da869-e264-4a8f-8d7e-fcd01c18f301 - name: Detect web traffic to dynamic domain providers - type: splunk -id: 8169f17b-ef68-4b59-aae8-586907301221 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-09-06' -name: Dynamic DNS narrative: Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or @@ -30,15 +16,15 @@ narrative: Dynamic DNS services (DDNS) are legitimate low-cost or free services more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html - - https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ - - http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ - - https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html -spec_version: 2 -usecase: Security Monitoring -version: '2.0' +- https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html +- https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/ +- http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/ +- https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html +tags: + analytics_story: Dynamic DNS + usecase: Security Monitoring + category: + - Malware diff --git a/stories/emotet.yml b/stories/emotet.yml deleted file mode 100644 index ef81af6a9f..0000000000 --- a/stories/emotet.yml +++ /dev/null @@ -1,72 +0,0 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-09-11' -description: Detect rarely used executables, specific registry paths that may confer - malware survivability and persistence, instances where cmd.exe is used to launch - script interpreters, and other indicators that the Emotet financial malware has - compromised your environment. -detections: - - detection_id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac - name: Detect Rare Executables - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-95dbb162039e - name: Detect Use of cmd.exe to Launch Script Interpreters - type: splunk - - detection_id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893 - name: Prohibited Software On Endpoint - type: splunk - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b936378 - name: SMB Traffic Spike - type: splunk - - detection_id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 - name: SMB Traffic Spike - MLTK - type: splunk - - detection_id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084 - name: Suspicious Email Attachment Extensions - type: splunk - - detection_id: 56e877a6-1455-4479-ada6-0550dc1e22f8 - name: Email Attachments With Lots Of Spaces - type: splunk - - detection_id: 1297fb80-f42a-4q4a-9c8b-78c061417cf6 - name: Detection of tools built by NirSoft - type: splunk -id: bb9f5ed2-916e-4364-bb6d-91c310efcf52 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-01-27' -name: Emotet Malware (DHS Report TA18-201A) -narrative: 'The trojan downloader known as Emotet first surfaced in 2014, when it - was discovered targeting the banking industry to steal credentials. However, according - to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), - Emotet has evolved far beyond those beginnings to become what a ThreatPost article - called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For - example, in early 2018, Emotet was found to be using its loader function to spread - the Quakbot and Ransomware variants. \ - - According to the TA, the the malware continues to be among the most costly and destructive - malware affecting the private and public sectors. Researchers have linked it to - the threat group Mealybug, which has also been on the security communitys radar - since 2014.\ - - The searches in this Analytic Story will help you find executables that are rarely - used in your environment, specific registry paths that malware often uses to ensure - survivability and persistence, instances where cmd.exe is used to launch script - interpreters, and other indicators that Emotet or other malware has compromised - your environment. ' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://www.us-cert.gov/ncas/alerts/TA18-201A - - https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf - - https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' diff --git a/stories/emotet_malware__dhs_report_ta18_201a_.yml b/stories/emotet_malware__dhs_report_ta18_201a_.yml new file mode 100644 index 0000000000..4f26827dfa --- /dev/null +++ b/stories/emotet_malware__dhs_report_ta18_201a_.yml @@ -0,0 +1,37 @@ +name: 'Emotet Malware DHS Report TA18-201A ' +id: bb9f5ed2-916e-4364-bb6d-91c310efcf52 +version: 1 +date: '2020-01-27' +description: Detect rarely used executables, specific registry paths that may confer + malware survivability and persistence, instances where cmd.exe is used to launch + script interpreters, and other indicators that the Emotet financial malware has + compromised your environment. +narrative: 'The trojan downloader known as Emotet first surfaced in 2014, when it + was discovered targeting the banking industry to steal credentials. However, according + to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), + Emotet has evolved far beyond those beginnings to become what a ThreatPost article + called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For + example, in early 2018, Emotet was found to be using its loader function to spread + the Quakbot and Ransomware variants. \ + + According to the TA, the the malware continues to be among the most costly and destructive + malware affecting the private and public sectors. Researchers have linked it to + the threat group Mealybug, which has also been on the security communitys radar + since 2014.\ + + The searches in this Analytic Story will help you find executables that are rarely + used in your environment, specific registry paths that malware often uses to ensure + survivability and persistence, instances where cmd.exe is used to launch script + interpreters, and other indicators that Emotet or other malware has compromised + your environment. ' +author: Bhavin Patel, Splunk +type: ESCU +references: +- https://www.us-cert.gov/ncas/alerts/TA18-201A +- https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf +- https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html +tags: + analytics_story: 'Emotet Malware DHS Report TA18-201A ' + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/hidden_cobra_malware.yml b/stories/hidden_cobra_malware.yml index 0283ca376e..5fc84b1ac7 100644 --- a/stories/hidden_cobra_malware.yml +++ b/stories/hidden_cobra_malware.yml @@ -1,49 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-06-14' +name: Hidden Cobra Malware +id: baf7580b-d4b4-4774-8173-7d198e9da335 +version: 2 +date: '2020-01-22' description: Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. -detections: - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b936378 - name: SMB Traffic Spike - type: splunk - - detection_id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 - name: SMB Traffic Spike - MLTK - type: splunk - - detection_id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 - name: First time seen command line argument - type: splunk - - detection_id: 7f5fb3e1-4209-414-90db-0ec21b936378 - name: Detect Outbound SMB Traffic - type: splunk - - detection_id: 272b8407-842d-4b3d-bead-a704584003d3 - name: Remote Desktop Network Traffic - type: splunk - - detection_id: f5939373-8054-40ad-8c64-cec478a22a4a - name: Remote Desktop Process Running On System - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 - name: DNS Query Length With High Standard Deviation - type: splunk - - detection_id: qw9919ed-fe5f-492c-b139-151bb162140e - name: Create or delete hidden shares using net.exe - type: splunk - - detection_id: 57f76b8a-32f0-42ed-b358-d9fa3ca7bac8 - name: Suspicious File Write - type: splunk - - detection_id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 - name: DNS Query Length Outliers - MLTK - type: splunk -id: baf7580b-d4b4-4774-8173-7d198e9da335 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-01-22' -name: Hidden Cobra Malware narrative: 'North Korea''s government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group''s activity, which the US government refers to as "Hidden Cobra," has surreptitiously @@ -71,13 +33,13 @@ narrative: 'North Korea''s government-sponsored "cyber army" has been slowly bui malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity - - https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' +- https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity +- https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf +tags: + analytics_story: Hidden Cobra Malware + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/host_redirection.yml b/stories/host_redirection.yml index 3b16a8b7cb..346b913191 100644 --- a/stories/host_redirection.yml +++ b/stories/host_redirection.yml @@ -1,28 +1,11 @@ -category: - - Abuse -channel: ESCU -creation_date: '2017-06-18' +name: Host Redirection +id: 2e8948a5-5239-406b-b56b-6c50fe268af4 +version: 1 +date: '2017-09-14' description: Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. -detections: - - detection_id: 74ec6f18-604b-4202-a567-86b2066be3ce - name: Clients Connecting to Multiple DNS Servers - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 - name: DNS Query Requests Resolved by Unauthorized DNS Servers - type: splunk - - detection_id: 06a6fc63-a72d-41dc-8736-7e3dd9612116 - name: Windows hosts file modification - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50fe268af4 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-14' -name: Host Redirection narrative: Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) @@ -31,12 +14,12 @@ narrative: Attackers will often attempt to manipulate client communications for site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/ +tags: + analytics_story: Host Redirection + usecase: Advanced Threat Detection + category: + - Abuse diff --git a/stories/jboss_vulnerabilities.yml b/stories/jboss_vulnerability.yml similarity index 88% rename from stories/jboss_vulnerabilities.yml rename to stories/jboss_vulnerability.yml index 5d7958f397..fe822e0c1d 100644 --- a/stories/jboss_vulnerabilities.yml +++ b/stories/jboss_vulnerability.yml @@ -1,25 +1,11 @@ -category: - - Vulnerability -channel: ESCU -creation_date: '2016-10-04' +name: JBoss Vulnerability +id: 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd +version: 1 +date: '2017-09-14' description: In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. -detections: - - detection_id: c8bff7a4-11ea-4416-a27d-c5bca472913d - name: Detect malicious requests to exploit JBoss servers - type: splunk - - detection_id: 104658f4-afdc-499e-9719-17243f982681 - name: Detect attackers scanning for vulnerable JBoss servers - type: splunk -id: 1f5294cb-b85f-4c2d-9c58-ffcf248f52bd -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-14' -name: JBoss Vulnerability narrative: 'This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with @@ -101,12 +87,12 @@ narrative: 'This Analytic Story looks for probing and exploitation attempts targ connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html +tags: + analytics_story: JBoss Vulnerability + usecase: Advanced Threat Detection + category: + - Vulnerability diff --git a/stories/kubernetes_scanning_activity.yml b/stories/kubernetes_scanning_activity.yml index b2deb17c8c..be38a9c8a1 100644 --- a/stories/kubernetes_scanning_activity.yml +++ b/stories/kubernetes_scanning_activity.yml @@ -1,33 +1,20 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2020-03-24' -description: This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. -detections: - - detection_id: db5957ec-0144-4c56-b512-9dccbe7a2d26 - name: GCP Kubernetes cluster scan detection - type: splunk - - detection_id: 294c4686-63dd-4fe6-93a2-ca807626704a - name: Amazon EKS Kubernetes cluster scan detection - type: splunk - - detection_id: dbfca1dd-b8e5-4ba4-be0e-e565e5d62002 - name: Amazon EKS Kubernetes Pod scan detection - type: splunk -id: a9ef59cf-e981-4e66-9eef-bb049f695c09 -maintainers: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -modification_date: '2020-04-15' name: Kubernetes Scanning Activity -narrative: Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. -original_authors: - - company: Splunk - email: rsoto@splunk.com - name: Rod Soto -spec_version: 2 -references: [] -usecase: Security Monitoring -version: '1.0' +id: a9ef59cf-e981-4e66-9eef-bb049f695c09 +version: 1 +date: '2020-04-15' +description: This story addresses detection against Kubernetes cluster fingerprint + scan and attack by providing information on items such as source ip, user agent, + cluster names. +narrative: Kubernetes is the most used container orchestration platform, this orchestration + platform contains sensitve information and management priviledges of production + workloads, microservices and applications. These searches allow operator to detect + suspicious unauthenticated requests from the internet to kubernetes cluster. +author: Rod Soto, Splunk +type: ESCU references: - - https://github.com/splunk/cloud-datamodel-security-research +- https://github.com/splunk/cloud-datamodel-security-research +tags: + analytics_story: Kubernetes Scanning Activity + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/lateral_movement.yml b/stories/lateral_movement.yml index 7bbfb88f0d..9d7d1efff9 100644 --- a/stories/lateral_movement.yml +++ b/stories/lateral_movement.yml @@ -1,30 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2016-09-13' +name: Lateral Movement +id: 399d65dc-1f08-499b-a259-aad9051f38ad +version: 2 +date: '2020-02-04' description: Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. -detections: - - detection_id: 1297fb80-f42a-4b4a-9c8a-88c066237cf6 - name: Schtasks scheduling job on remote system - type: splunk - - detection_id: f5939373-8054-40ad-8c64-cec478a22a4a - name: Remote Desktop Process Running On System - type: splunk - - detection_id: 272b8407-842d-4b3d-bead-a704584003d3 - name: Remote Desktop Network Traffic - type: splunk - - detection_id: f5939373-8054-40ad-8c64-cec478a22a4b - name: Detect Activity Related to Pass the Hash Attacks - type: splunk -id: 399d65dc-1f08-499b-a259-aad9051f38ad -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2020-02-04' -name: Lateral Movement narrative: "Once attackers gain a foothold within an enterprise, they will seek to\ \ expand their accesses and leverage techniques that facilitate lateral movement.\ \ Attackers will often spend quite a bit of time and effort moving laterally. Because\ @@ -49,13 +29,12 @@ narrative: "Once attackers gain a foothold within an enterprise, they will seek \ hosts. \\\n It is also important to collect authentication logs for each host,\ \ to ensure that the offending accounts are well-documented. Analysts should account\ \ for all processes to ensure that the attackers did not install unauthorized software." -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.binarydefense.com/reliably-detecting-pass-the-hash/ - - https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' +- https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html +tags: + analytics_story: Lateral Movement + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/malicious_powershell.yml b/stories/malicious_powershell.yml index 179c11f175..e32686a527 100644 --- a/stories/malicious_powershell.yml +++ b/stories/malicious_powershell.yml @@ -1,34 +1,11 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2016-09-18' +name: Malicious PowerShell +id: 2c8ff66e-0b57-42af-8ad7-912438a403fc +version: 4 +date: '2017-08-23' description: Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. -detections: - - detection_id: ee18ed37-0802-4268-9435-b3b91aaa18db - name: Malicious PowerShell Process - Connect To Internet With Hidden Window - type: splunk - - detection_id: c4db14d9-7909-48b4-a054-aa14d89dbb19 - name: Malicious PowerShell Process - Encoded Command - type: splunk - - detection_id: 2cdb91d2-542c-497f-b252-be495e71f38c - name: Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - type: splunk - - detection_id: cde75cf6-3c7a-4dd6-af01-27cdb4511fd4 - name: Malicious PowerShell Process With Obfuscation Techniques - type: splunk - - detection_id: c2590137-0b08-4985-9ec5-6ae23d92f63d - name: Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - type: splunk -id: 2c8ff66e-0b57-42af-8ad7-912438a403fc -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-08-23' -name: Malicious PowerShell narrative: 'The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these @@ -83,13 +60,13 @@ narrative: 'The searches in this Analytic Story monitor for parameters often use we suggest reviewing the browsing activity from that system around the time of the event. If categories are given for the URLs visited, that can help you zero in on possible malicious sites.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ - - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '4.0' +- https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/ +- https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ +tags: + analytics_story: Malicious PowerShell + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/monitor_backups.yml b/stories/monitor_backup_solution.yml similarity index 56% rename from stories/monitor_backups.yml rename to stories/monitor_backup_solution.yml index 56eafd5d71..c581fc63fe 100644 --- a/stories/monitor_backups.yml +++ b/stories/monitor_backup_solution.yml @@ -1,24 +1,10 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-06-15' +name: Monitor Backup Solution +id: abe807c7-1eb6-4304-ac32-6e7aacdb891d +version: 1 +date: '2017-09-12' description: Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. -detections: - - detection_id: a34aae96-ccf8-4aaa-952c-3ea21444444f - name: Unsuccessful Netbackup backups - type: splunk - - detection_id: a34aae96-ccf8-4aef-952c-3ea214444440 - name: Extended Period Without Successful Netbackup Backups - type: splunk -id: abe807c7-1eb6-4304-ac32-6e7aacdb891d -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-12' -name: Monitor Backup Solution narrative: Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. @@ -26,12 +12,12 @@ narrative: Having backups is a standard best practice that helps ensure continui have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ -spec_version: 2 -usecase: Compliance -version: '1.0' +- https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/ +tags: + analytics_story: Monitor Backup Solution + usecase: Compliance + category: + - Best Practices diff --git a/stories/detect_unauthorized_processes.yml b/stories/monitor_for_unauthorized_software.yml similarity index 70% rename from stories/detect_unauthorized_processes.yml rename to stories/monitor_for_unauthorized_software.yml index 022c868b8d..fd33be58d2 100644 --- a/stories/detect_unauthorized_processes.yml +++ b/stories/monitor_for_unauthorized_software.yml @@ -1,20 +1,9 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-06-26' +name: Monitor for Unauthorized Software +id: 8892a655-6205-43f7-abba-06460e38c8ae +version: 1 +date: '2017-09-15' description: 'Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. ' -detections: - - detection_id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893 - name: Prohibited Software On Endpoint - type: splunk -id: 8892a655-6205-43f7-abba-06460e38c8ae -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2017-09-15' -name: Monitor for Unauthorized Software narrative: 'It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within @@ -28,12 +17,12 @@ narrative: 'It is critical to identify unauthorized software and processes runni understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. ' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ -spec_version: 2 -usecase: Compliance -version: '1.0' +- https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ +tags: + analytics_story: Monitor for Unauthorized Software + usecase: Compliance + category: + - Best Practices diff --git a/stories/monitor_for_updates.yml b/stories/monitor_for_updates.yml index 195709f1df..6394705018 100644 --- a/stories/monitor_for_updates.yml +++ b/stories/monitor_for_updates.yml @@ -1,21 +1,10 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-08-15' +name: Monitor for Updates +id: 9ef8d677-7b52-4213-a038-99cfc7acc2d8 +version: 1 +date: '2017-09-15' description: Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. -detections: - - detection_id: 1a77c08c-2f56-409c-a2d3-7d64617edd4f - name: No Windows Updates in a time frame - type: splunk -id: 9ef8d677-7b52-4213-a038-99cfc7acc2d8 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-15' -name: Monitor for Updates narrative: 'It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important @@ -29,12 +18,12 @@ narrative: 'It is a common best practice to ensure that endpoints are being patc be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://learn.cisecurity.org/20-controls-download -spec_version: 2 -usecase: Compliance -version: '1.0' +- https://learn.cisecurity.org/20-controls-download +tags: + analytics_story: Monitor for Updates + usecase: Compliance + category: + - Best Practices diff --git a/stories/netsh_abuse.yml b/stories/netsh_abuse.yml index d799f7e603..f294b5483c 100644 --- a/stories/netsh_abuse.yml +++ b/stories/netsh_abuse.yml @@ -1,24 +1,10 @@ -category: - - Abuse -channel: ESCU -creation_date: '2017-01-04' +name: Netsh Abuse +id: 2b1800dd-92f9-47ec-a981-fdf1351e5f65 +version: 1 +date: '2017-01-05' description: Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. -detections: - - detection_id: b89919ed-fe5f-492c-b139-95dbb162041e - name: Processes created by netsh - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-95dbb162040e - name: Processes launching netsh - type: splunk -id: 2b1800dd-92f9-47ec-a981-fdf1351e5f65 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-01-05' -name: Netsh Abuse narrative: 'It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you @@ -28,14 +14,14 @@ narrative: 'It is a common practice for attackers of all types to leverage nativ host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://technet.microsoft.com/library/bb490939.aspx - - https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html - - http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://technet.microsoft.com/library/bb490939.aspx +- https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html +- http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html +tags: + analytics_story: Netsh Abuse + usecase: Advanced Threat Detection + category: + - Abuse diff --git a/stories/orangeworm.yml b/stories/orangeworm_attack_group.yml similarity index 69% rename from stories/orangeworm.yml rename to stories/orangeworm_attack_group.yml index c6b3e676c3..a72bf7c573 100644 --- a/stories/orangeworm.yml +++ b/stories/orangeworm_attack_group.yml @@ -1,26 +1,9 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-06-14' +name: Orangeworm Attack Group +id: bb9f5ed2-916e-4364-bb6d-97c370efcf52 +version: 2 +date: '2020-01-22' description: Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. -detections: - - detection_id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 - name: First time seen command line argument - type: splunk - - detection_id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d - name: Sc.exe Manipulating Windows Services - type: splunk - - detection_id: 823136f2-d755-4b6d-ae04-372b486a5808 - name: First Time Seen Running Windows Service - type: splunk -id: bb9f5ed2-916e-4364-bb6d-97c370efcf52 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2020-01-22' -name: Orangeworm Attack Group narrative: 'In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with @@ -48,13 +31,13 @@ narrative: 'In May of 2018, the attack group Orangeworm was implicated for insta arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia - - https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' +- https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia +- https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/ +tags: + analytics_story: Orangeworm Attack Group + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/phishing_payloads.yml b/stories/phishing_payloads.yml index 2b33128ba2..62fa65e2a4 100644 --- a/stories/phishing_payloads.yml +++ b/stories/phishing_payloads.yml @@ -1,23 +1,9 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2019-04-29' +name: Phishing Payloads +id: 57226b40-94f3-4ce5-b101-a75f67759c27 +version: 1 +date: '2019-04-29' description: Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. -detections: - - detection_id: a51bfe1a-94f0-4822-b1e4-16ae10145893 - name: Detect Oulook.exe writing a .zip file - type: splunk - - detection_id: 5d814af1-1041-47b5-a9ac-d754e82e9a26 - name: Suspicious LNK file launching a process - type: splunk -id: 57226b40-94f3-4ce5-b101-a75f67759c27 -maintainers: - - company: Splunk - email: research@splunk.com - name: Splunk Research Team -modification_date: '2019-04-29' -name: Phishing Payloads narrative: 'Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ @@ -55,12 +41,12 @@ narrative: 'Despite its simplicity, phishing remains the most pervasive and dang This Analytic Story focuses on detecting signs that a malicious payload has been injected into your environment. For example, one search detects outlook.exe writing a .zip file. Another looks for suspicious .lnk files launching processes.' -original_authors: - - company: Splunk - email: research@splunk.com - name: Splunk Research Team +author: Splunk Research Team, Splunk +type: ESCU references: - - https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html +tags: + analytics_story: Phishing Payloads + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/mudcarp.yml b/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml similarity index 69% rename from stories/mudcarp.yml rename to stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml index c709025000..943ec649c4 100644 --- a/stories/mudcarp.yml +++ b/stories/possible_backdoor_activity_associated_with_mudcarp_espionage_campaigns.yml @@ -1,32 +1,9 @@ -category: - - Adversary Tactics -channel: ESCU channel -creation_date: '2018-07-24' +name: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns +id: 988C59C5-0A1C-45B6-A555-0C62276E327E +version: 1 +date: '2020-01-22' description: Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. -detections: - - detection_id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 - name: First time seen command line argument - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: ee18ed37-0802-4268-9435-b3b91aaa18db - name: Malicious PowerShell Process - Connect To Internet With Hidden Window - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6a4264e7f - name: Unusually Long Command Line - type: splunk - - detection_id: 57edaefa-a73b-45e5-bbae-f39c1473f941 - name: Unusually Long Command Line - MLTK - type: splunk -id: 988C59C5-0A1C-45B6-A555-0C62276E327E -maintainers: - - company: iDefense - email: iDefense.IntelOps@accenture.com - name: iDefense Cyber Espionage Team -modification_date: '2020-01-22' -name: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns narrative: 'This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon @@ -97,13 +74,13 @@ narrative: 'This story was created as a joint effort between iDefense and Splunk 1. b8ec65dab97cdef3cd256cc4753f0c54\ 1. 04d83cd3813698de28cfbba326d7647c' -original_authors: - - company: iDefense - email: iDefense.IntelOps@accenture.com - name: iDefense Cyber Espionage Team +author: iDefense Cyber Espionage Team, iDefense +type: ESCU references: - - https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ - - http://blog.amossys.fr/badflick-is-not-so-bad.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/ +- http://blog.amossys.fr/badflick-is-not-so-bad.html +tags: + analytics_story: Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/prohibited_traffic_and_protocol_mismatch.yml b/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml similarity index 52% rename from stories/prohibited_traffic_and_protocol_mismatch.yml rename to stories/prohibited_traffic_allowed_or_protocol_mismatch.yml index 85c8a6a65f..4e41c6b110 100644 --- a/stories/prohibited_traffic_and_protocol_mismatch.yml +++ b/stories/prohibited_traffic_allowed_or_protocol_mismatch.yml @@ -1,30 +1,10 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-04-18' +name: Prohibited Traffic Allowed or Protocol Mismatch +id: 6d13121c-90f3-446d-8ac3-27efbbc65218 +version: 1 +date: '2017-09-11' description: Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. -detections: - - detection_id: ea688274-9c06-4473-b951-e4cb7a5d7a45 - name: TOR Traffic - type: splunk - - detection_id: ce5a0962-849f-4720-a678-753fe6674479 - name: Prohibited Network Traffic Allowed - type: splunk - - detection_id: 54dc1265-2f74-4b6d-b30d-49eb506a31b3 - name: Protocol or Port Mismatch - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk -id: 6d13121c-90f3-446d-8ac3-27efbbc65218 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-11' -name: Prohibited Traffic Allowed or Protocol Mismatch narrative: A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. @@ -33,12 +13,12 @@ narrative: A traditional security best practice is to control the ports, protoco policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/ +tags: + analytics_story: Prohibited Traffic Allowed or Protocol Mismatch + usecase: Security Monitoring + category: + - Best Practices diff --git a/stories/ransomware.yml b/stories/ransomware.yml index 1cc5e35a96..9293bd2276 100644 --- a/stories/ransomware.yml +++ b/stories/ransomware.yml @@ -1,73 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2017-07-24' +name: Ransomware +id: cf309d0d-d4aa-4fbb-963d-1e79febd3756 +version: 1 +date: '2020-02-04' description: Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. -detections: - - detection_id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a - name: Windows Event Log Cleared - type: splunk - - detection_id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f - name: Suspicious wevtutil Usage - type: splunk - - detection_id: b6e0ff70-b122-4227-9368-4cf322ab43c3 - name: USN Journal Deletion - type: splunk - - detection_id: b89919ed-ee5f-492c-b139-95dbb162039e - name: Deleting Shadow Copies - type: splunk - - detection_id: fdb0f805-74e4-4539-8c00-618927333aae - name: Spike in File Writes - type: splunk - - detection_id: ce5a0962-849f-4720-a678-753fe6674479 - name: Prohibited Network Traffic Allowed - type: splunk - - detection_id: 7f5fb3e1-4209-4914-90db-0ec21b936378 - name: SMB Traffic Spike - type: splunk - - detection_id: d25773ba-9ad8-48d1-858e-07ad0bbeb828 - name: SMB Traffic Spike - MLTK - type: splunk - - detection_id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec - name: Common Ransomware Extensions - type: splunk - - detection_id: ada0f478-84a8-4641-a3f1-d82362d6bd71 - name: Common Ransomware Notes - type: splunk - - detection_id: a34aae96-ccf8-4aef-952c-3ea21444444d - name: System Processes Run From Unexpected Locations - type: splunk - - detection_id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da - name: Remote Process Instantiation via WMI - type: splunk - - detection_id: ea688274-9c06-4473-b951-e4cb7a5d7a45 - name: TOR Traffic - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6a4264e7f - name: Unusually Long Command Line - type: splunk - - detection_id: 57edaefa-a73b-45e5-bbae-f39c1473f941 - name: Unusually Long Command Line - MLTK - type: splunk - - detection_id: 1297fb80-f42a-4b4a-9c8b-78c066437cf6 - name: Scheduled tasks used in BadRabbit ransomware - type: splunk - - detection_id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6 - name: Schtasks used for forcing a reboot - type: splunk -id: cf309d0d-d4aa-4fbb-963d-1e79febd3756 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2020-02-04' -name: Ransomware narrative: Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect @@ -77,13 +15,13 @@ narrative: Ransomware is an ever-present risk to the enterprise, wherein an infe functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ - - https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.1' +- https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/ +- https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html +tags: + analytics_story: Ransomware + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/routers_and_infrastructure.yml b/stories/router_and_infrastructure_security.yml similarity index 61% rename from stories/routers_and_infrastructure.yml rename to stories/router_and_infrastructure_security.yml index b79d1c9ae3..b72afd01ba 100644 --- a/stories/routers_and_infrastructure.yml +++ b/stories/router_and_infrastructure_security.yml @@ -1,21 +1,10 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2017-06-01' +name: Router and Infrastructure Security +id: 91c676cf-0b23-438d-abee-f6335e177e77 +version: 1 +date: '2017-09-12' description: Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. -detections: - - detection_id: 104658f4-afdc-499e-9719-17243rr826f1 - name: Detect New Login Attempts to Routers - type: splunk -id: 91c676cf-0b23-438d-abee-f6335e177e77 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-12' -name: Router & Infrastructure Security narrative: 'Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon @@ -26,13 +15,13 @@ narrative: 'Networking devices, such as routers and switches, are often overlook are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company''s internal infrastructure— effectively increasing the attack surface and accessing private services/data.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html - - https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html +- https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html +tags: + analytics_story: Router and Infrastructure Security + usecase: Security Monitoring + category: + - Best Practices diff --git a/stories/samsam.yml b/stories/samsam_ransomware.yml similarity index 55% rename from stories/samsam.yml rename to stories/samsam_ransomware.yml index 74fbdc2d20..b6c5fa9754 100644 --- a/stories/samsam.yml +++ b/stories/samsam_ransomware.yml @@ -1,58 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-12-13' +name: SamSam Ransomware +id: c4b89506-fbcf-4cb7-bfd6-527e54789604 +version: 1 +date: '2018-12-13' description: Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. -detections: - - detection_id: b89919ed-ee5f-492c-b139-95dbb162039e - name: Deleting Shadow Copies - type: splunk - - detection_id: fdb0f805-74e4-4539-8c00-618927333aae - name: Spike in File Writes - type: splunk - - detection_id: a9e5c5db-db11-43ca-86a8-c852d1b2c0ec - name: Common Ransomware Extensions - type: splunk - - detection_id: ada0f478-84a8-4641-a3f1-d82362d6bd71 - name: Common Ransomware Notes - type: splunk - - detection_id: a51bfe1a-94f0-48cc-b4e4-b6ae50145893 - name: Prohibited Software On Endpoint - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-151xb162040e - name: Detect PsExec With accepteula Flag - type: splunk - - detection_id: 272b8407-842d-4b3d-bead-a704584003d3 - name: Remote Desktop Network Traffic - type: splunk - - detection_id: 104658f4-afdc-499e-9719-17243f982681 - name: Detect attackers scanning for vulnerable JBoss servers - type: splunk - - detection_id: c8bff7a4-11ea-4416-a27d-c5bca472913d - name: Detect malicious requests to exploit JBoss servers - type: splunk - - detection_id: a98727cc-286b-4ff2-b898-41df64695923 - name: Remote Desktop Network Bruteforce - type: splunk - - detection_id: 02c6cfc2-ae66-4735-bfc7-6291da834cbf - name: File with Samsam Extension - type: splunk - - detection_id: 69c12d59-d951-431e-ab77-ec426b8d65e6 - name: Samsam Test File Write - type: splunk - - detection_id: 503d17cb-9eab-4cf8-a20e-01d5c6987ae3 - name: Batch File Write to System32 - type: splunk -id: c4b89506-fbcf-4cb7-bfd6-527e54789604 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2018-12-13' -name: SamSam Ransomware narrative: 'The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost @@ -87,14 +40,14 @@ narrative: 'The first version of the SamSam ransomware (a.k.a. Samas or SamsamCr of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ - - https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ - - https://thehackernews.com/2018/07/samsam-ransomware-attacks.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/ +- https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/ +- https://thehackernews.com/2018/07/samsam-ransomware-attacks.html +tags: + analytics_story: SamSam Ransomware + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/spectre_meltdown.yml b/stories/spectre_and_meltdown_vulnerabilities.yml similarity index 52% rename from stories/spectre_meltdown.yml rename to stories/spectre_and_meltdown_vulnerabilities.yml index 9acb30f1fa..eb7b3c0bda 100644 --- a/stories/spectre_meltdown.yml +++ b/stories/spectre_and_meltdown_vulnerabilities.yml @@ -1,30 +1,19 @@ -category: - - Vulnerability -channel: ESCU -creation_date: '2018-01-08' +name: Spectre And Meltdown Vulnerabilities +id: 6d3306f6-bb2b-4219-8609-8efad64032f2 +version: 1 +date: '2018-01-08' description: Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. -detections: - - detection_id: 354be8e0-32cd-4da0-8c47-796de13b60ea - name: Spectre and Meltdown Vulnerable Systems - type: splunk -id: 6d3306f6-bb2b-4219-8609-8efad64032f2 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-01-08' -name: Spectre And Meltdown Vulnerabilities narrative: Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://meltdownattack.com/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://meltdownattack.com/ +tags: + analytics_story: Spectre And Meltdown Vulnerabilities + usecase: Security Monitoring + category: + - Vulnerability diff --git a/stories/splunk_vulnerabilities.yml b/stories/splunk_enterprise_vulnerability.yml similarity index 71% rename from stories/splunk_vulnerabilities.yml rename to stories/splunk_enterprise_vulnerability.yml index 97e1e46d20..65e9efc461 100644 --- a/stories/splunk_vulnerabilities.yml +++ b/stories/splunk_enterprise_vulnerability.yml @@ -1,22 +1,11 @@ -category: - - Vulnerability -channel: ESCU -creation_date: '2016-09-13' +name: Splunk Enterprise Vulnerability +id: 4e692b96-de2d-4bd1-9105-37e2368a8db1 +version: 1 +date: '2017-09-19' description: Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. -detections: - - detection_id: d199fb99-2312-451a-9daa-e5efa6ed76a7 - name: Open Redirect in Splunk Web - type: splunk -id: 4e692b96-de2d-4bd1-9105-37e2368a8db1 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-19' -name: Splunk Enterprise Vulnerability narrative: 'This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ @@ -42,13 +31,13 @@ narrative: 'This Analytic Story is associated with CVE-2016-4859, an open-redire is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - http://www.splunk.com/view/SP-CAAAPQ6#announce - - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- http://www.splunk.com/view/SP-CAAAPQ6#announce +- https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859 +tags: + analytics_story: Splunk Enterprise Vulnerability + usecase: Security Monitoring + category: + - Vulnerability diff --git a/stories/splunk_information_disclosure.yml b/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml similarity index 74% rename from stories/splunk_information_disclosure.yml rename to stories/splunk_enterprise_vulnerability_cve_2018_11409.yml index d6038ffe38..b86d68483a 100644 --- a/stories/splunk_information_disclosure.yml +++ b/stories/splunk_enterprise_vulnerability_cve_2018_11409.yml @@ -1,21 +1,10 @@ -category: - - Vulnerability -channel: ESCU -creation_date: '2018-06-14' +name: Splunk Enterprise Vulnerability CVE-2018-11409 +id: 1fc34cbc-34e9-43ba-87ab-6811c9e95400 +version: 1 +date: '2018-06-14' description: Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. -detections: - - detection_id: f6a26b7b-7e80-4963-a9a8-d836e7534ebd - name: Splunk Enterprise Information Disclosure - type: splunk -id: 1fc34cbc-34e9-43ba-87ab-6811c9e95400 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-06-14' -name: Splunk Enterprise Vulnerability CVE-2018-11409 narrative: 'Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). @@ -36,14 +25,14 @@ narrative: 'Although there have been no reports of it being exploited, Splunk En in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://nvd.nist.gov/vuln/detail/CVE-2018-11409 - - https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings - - https://www.exploit-db.com/exploits/44865/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://nvd.nist.gov/vuln/detail/CVE-2018-11409 +- https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings +- https://www.exploit-db.com/exploits/44865/ +tags: + analytics_story: Splunk Enterprise Vulnerability CVE-2018-11409 + usecase: Security Monitoring + category: + - Vulnerability diff --git a/stories/sql_injection.yml b/stories/sql_injection.yml index 71a21fe09b..d146c538a1 100644 --- a/stories/sql_injection.yml +++ b/stories/sql_injection.yml @@ -1,34 +1,23 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2016-09-13' +name: SQL Injection +id: 4f6632f5-449c-4686-80df-57625f59bab3 +version: 1 +date: '2017-09-19' description: Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. -detections: - - detection_id: e0aad4cf-0790-423b-8328-7564d0d938f9 - name: SQL Injection with Long URLs - type: splunk -id: 4f6632f5-449c-4686-80df-57625f59bab3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-19' -name: SQL Injection narrative: 'It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://capec.mitre.org/data/definitions/66.html - - https://www.incapsula.com/web-application-security/sql-injection.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://capec.mitre.org/data/definitions/66.html +- https://www.incapsula.com/web-application-security/sql-injection.html +tags: + analytics_story: SQL Injection + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_aws_ec2_activities.yml b/stories/suspicious_aws_ec2_activities.yml new file mode 100644 index 0000000000..d94a36ca18 --- /dev/null +++ b/stories/suspicious_aws_ec2_activities.yml @@ -0,0 +1,25 @@ +name: Suspicious AWS EC2 Activities +id: 2e8948a5-5239-406b-b56b-6c50f1268af3 +version: 1 +date: '2018-02-09' +description: Use the searches in this Analytic Story to monitor your AWS EC2 instances + for evidence of anomalous activity and suspicious behaviors, such as EC2 instances + that originate from unusual locations or those launched by previously unseen users + (among others). Included investigative searches will help you probe more deeply, + when the information warrants it. +narrative: AWS CloudTrail is an AWS service that helps you enable governance, compliance, + and risk auditing within your AWS account. Actions taken by a user, role, or an + AWS service are recorded as events in CloudTrail. It is crucial for a company to + monitor events and actions taken in the AWS Console, AWS command-line interface, + and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. + This Analytic Story identifies suspicious activities in your AWS EC2 instances and + helps you respond and investigate those activities. +author: Bhavin Patel, Splunk +type: ESCU +references: +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +tags: + analytics_story: Suspicious AWS EC2 Activities + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/suspicious_aws_login_activities.yml b/stories/suspicious_aws_login_activities.yml new file mode 100644 index 0000000000..e04a2e5459 --- /dev/null +++ b/stories/suspicious_aws_login_activities.yml @@ -0,0 +1,21 @@ +name: Suspicious AWS Login Activities +id: 2e8948a5-5239-406b-b56b-6c59f1268af3 +version: 1 +date: '2019-05-01' +description: 'Monitor your AWS authentication events using your CloudTrail logs. Searches + within this Analytic Story will help you stay aware of and investigate suspicious + logins. ' +narrative: It is important to monitor and control who has access to your AWS infrastructure. + Detecting suspicious logins to your AWS infrastructure will provide good starting + points for investigations. Abusive behaviors caused by compromised credentials can + lead to direct monetary costs, as you will be billed for any EC2 instances created + by the attacker. +author: Bhavin Patel, Splunk +type: ESCU +references: +- https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html +tags: + analytics_story: Suspicious AWS Login Activities + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_s3.yml b/stories/suspicious_aws_s3_activities.yml similarity index 58% rename from stories/aws_s3.yml rename to stories/suspicious_aws_s3_activities.yml index 493803dcbc..362f0b2d25 100644 --- a/stories/aws_s3.yml +++ b/stories/suspicious_aws_s3_activities.yml @@ -1,28 +1,11 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-06-25' +name: Suspicious AWS S3 Activities +id: 2e8948a5-5239-406b-b56b-6c50w3168af3 +version: 2 +date: '2018-07-24' description: Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. -detections: - - detection_id: 2a9b80d3-6340-4345-b5ad-290bf3d0dac4 - name: Detect New Open S3 buckets - type: splunk - - detection_id: 2a9b80d3-6340-4345-b5ad-291bq3d0daq4 - name: Detect S3 access from a new IP - type: splunk - - detection_id: ad12w478-84a8-4641-a3w1-e32372q4bd53 - name: Detect Spike in S3 Bucket deletion - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50w3168af3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-07-24' -name: Suspicious AWS S3 Activities narrative: 'As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ @@ -35,13 +18,13 @@ narrative: 'As cloud computing has exploded, so has the number of creative attac Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. ' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf - - https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ -spec_version: 2 -usecase: Security Monitoring -version: '2.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +- https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/ +tags: + analytics_story: Suspicious AWS S3 Activities + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/aws_suspcious_traffic.yml b/stories/suspicious_aws_traffic.yml similarity index 73% rename from stories/aws_suspcious_traffic.yml rename to stories/suspicious_aws_traffic.yml index ccb672683b..c241058937 100644 --- a/stories/aws_suspcious_traffic.yml +++ b/stories/suspicious_aws_traffic.yml @@ -1,21 +1,10 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-05-07' +name: Suspicious AWS Traffic +id: 2e8948a5-5239-406b-b56b-6c50f2168af3 +version: 1 +date: '2018-05-07' description: Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). -detections: - - detection_id: ada0f278-84a8-46w1-a3f1-w32372d4bd53 - name: Detect Spike in blocked Outbound Traffic from your AWS - type: splunk -id: 2e8948a5-5239-406b-b56b-6c50f2168af3 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-07' -name: Suspicious AWS Traffic narrative: "A virtual private cloud (VPC) is an on-demand managed cloud-computing\ \ service that isolates computing resources for each client. Inside the VPC container,\ \ the environment resembles a physical network. \\\nAmazon's VPC service enables\ @@ -31,12 +20,12 @@ narrative: "A virtual private cloud (VPC) is an on-demand managed cloud-computin \ in and out of your network and to alert you to suspicious activities.\\\nThe searches\ \ in this Analytic Story will monitor your AWS network traffic for evidence of anomalous\ \ activity and suspicious behaviors." -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/ +tags: + analytics_story: Suspicious AWS Traffic + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/suspicious_cmd_line_executions.yml b/stories/suspicious_cmd_line_executions.yml deleted file mode 100644 index fc6f98d9af..0000000000 --- a/stories/suspicious_cmd_line_executions.yml +++ /dev/null @@ -1,54 +0,0 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-10-09' -description: Leveraging the Windows command-line interface (CLI) is one of the most - common attack techniques--one that is also detailed in the MITRE ATT&CK framework. - Use this Analytic Story to help you identify unusual or suspicious use of the CLI - on Windows systems. -detections: - - detection_id: 9be56c82-b1cc-4318-87eb-q138afaaqa39 - name: First time seen command line argument - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6a4264e7f - name: Unusually Long Command Line - type: splunk - - detection_id: 57edaefa-a73b-45e5-bbae-f39c1473f941 - name: Unusually Long Command Line - MLTK - type: splunk - - detection_id: dcfd6b40-42f9-469d-a433-2e53f7486664 - name: Detect Prohibited Applications Spawning cmd.exe - type: splunk - - detection_id: b89919ed-fe5f-492c-b139-95dbb162039e - name: Detect Use of cmd.exe to Launch Script Interpreters - type: splunk - - detection_id: a34aae96-ccf8-4aef-952c-3ea21444444d - name: System Processes Run From Unexpected Locations - type: splunk -id: f4368ddf-d59f-4192-84f6-778ac5a3ffc7 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-02-03' -name: Suspicious Command-Line Executions -narrative: The ability to execute arbitrary commands via the Windows CLI is a primary - goal for the adversary. With access to the shell, an attacker can easily run scripts - and interact with the target system. Often, attackers may only have limited access - to the shell or may obtain access in unusual ways. In addition, malware may execute - and interact with the CLI in ways that would be considered unusual and inconsistent - with typical user activity. This provides defenders with opportunities to identify - suspicious use and investigate, as appropriate. This Analytic Story contains various - searches to help identify this suspicious activity, as well as others to aid you - in deeper investigation. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - https://attack.mitre.org/wiki/Technique/T1059 - - https://www.microsoft.com/en-us/wdsi/threats/macro-malware - - https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.1' diff --git a/stories/suspicious_command_line_executions.yml b/stories/suspicious_command_line_executions.yml new file mode 100644 index 0000000000..9c9febd2e3 --- /dev/null +++ b/stories/suspicious_command_line_executions.yml @@ -0,0 +1,28 @@ +name: Suspicious Command-Line Executions +id: f4368ddf-d59f-4192-84f6-778ac5a3ffc7 +version: 2 +date: '2020-02-03' +description: Leveraging the Windows command-line interface (CLI) is one of the most + common attack techniques--one that is also detailed in the MITRE ATT&CK framework. + Use this Analytic Story to help you identify unusual or suspicious use of the CLI + on Windows systems. +narrative: The ability to execute arbitrary commands via the Windows CLI is a primary + goal for the adversary. With access to the shell, an attacker can easily run scripts + and interact with the target system. Often, attackers may only have limited access + to the shell or may obtain access in unusual ways. In addition, malware may execute + and interact with the CLI in ways that would be considered unusual and inconsistent + with typical user activity. This provides defenders with opportunities to identify + suspicious use and investigate, as appropriate. This Analytic Story contains various + searches to help identify this suspicious activity, as well as others to aid you + in deeper investigation. +author: Bhavin Patel, Splunk +type: ESCU +references: +- https://attack.mitre.org/wiki/Technique/T1059 +- https://www.microsoft.com/en-us/wdsi/threats/macro-malware +- https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf +tags: + analytics_story: Suspicious Command-Line Executions + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_dns_traffic.yml b/stories/suspicious_dns_traffic.yml index ac7d895153..a0925d45f2 100644 --- a/stories/suspicious_dns_traffic.yml +++ b/stories/suspicious_dns_traffic.yml @@ -1,42 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2016-09-13' +name: Suspicious DNS Traffic +id: 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56 +version: 1 +date: '2017-09-18' description: Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. -detections: - - detection_id: 104658f4-afdc-499e-9719-17243f9826f1 - name: Excessive DNS Failures - type: splunk - - detection_id: 74ec6f18-604b-4202-a567-86b2066be3ce - name: Clients Connecting to Multiple DNS Servers - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f5 - name: DNS Query Length With High Standard Deviation - type: splunk - - detection_id: 1a67f15a-f4ff-4170-84e9-08cf6f75d6f6 - name: DNS Query Requests Resolved by Unauthorized DNS Servers - type: splunk - - detection_id: 05437c07-62f5-452e-afdc-04dd44815bb9 - name: Detect Long DNS TXT Record Response - type: splunk - - detection_id: 104658f4-afdc-499f-9719-17a43f9826f4 - name: Detection of DNS Tunnels - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5464g9f - name: Detect hosts connecting to dynamic domain providers - type: splunk - - detection_id: 85fbcfe8-9718-4911-adf6-7000d077a3a9 - name: DNS Query Length Outliers - MLTK - type: splunk -id: 3c3835c0-255d-4f9e-ab84-e29ec9ec9b56 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-18' -name: Suspicious DNS Traffic narrative: Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One @@ -46,14 +14,14 @@ narrative: Although DNS is one of the fundamental underlying protocols that make involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ - - http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680 - - https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/ +- http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680 +- https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454 +tags: + analytics_story: Suspicious DNS Traffic + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_emails.yml b/stories/suspicious_emails.yml index d5181c7ce9..17efc4676f 100644 --- a/stories/suspicious_emails.yml +++ b/stories/suspicious_emails.yml @@ -1,30 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-03-24' +name: Suspicious Emails +id: 2b1800dd-92f9-47ec-a981-fdf1351e5d55 +version: 1 +date: '2020-01-27' description: Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. -detections: - - detection_id: b2ea1f38-3a3e-4b8a-9cf1-82760d86a6b8 - name: Monitor Email For Brand Abuse - type: splunk - - detection_id: 473bd65f-06ca-4dfe-a2b8-ba04ab4a0084 - name: Suspicious Email Attachment Extensions - type: splunk - - detection_id: 56e877a6-1455-4479-ada6-0550dc1e22f8 - name: Email Attachments With Lots Of Spaces - type: splunk - - detection_id: 56e877a6-1455-4479-ad16-0550dc1e33f8 - name: Suspicious Email - UBA Anomaly - type: uba -id: 2b1800dd-92f9-47ec-a981-fdf1351e5d55 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-01-27' -name: Suspicious Emails narrative: 'It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk @@ -41,12 +21,12 @@ narrative: 'It is a common practice for attackers of all types to leverage targe attachment or clicking on a malicious URL)?This Analytic Story provides detection searches to identify suspicious emails, as well as contextual and investigative searches to help answer some of these questions.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/ +tags: + analytics_story: Suspicious Emails + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_mshta_activities.yml b/stories/suspicious_mshta_activity.yml similarity index 58% rename from stories/suspicious_mshta_activities.yml rename to stories/suspicious_mshta_activity.yml index 6e8a5894db..6c1a98a5a5 100644 --- a/stories/suspicious_mshta_activities.yml +++ b/stories/suspicious_mshta_activity.yml @@ -1,26 +1,9 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-08-07' +name: Suspicious MSHTA Activity +id: 2b1800dd-92f9-47dd-a981-fdf13w1q5d55 +version: 1 +date: '2020-02-03' description: Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. -detections: - - detection_id: b89919ed-fe5f-492c-b139-95dqb161039e - name: Detect mshta.exe running scripts in command-line arguments - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: dcfd6b40-42f9-469d-a433-2e53f7486664 - name: Detect Prohibited Applications Spawning cmd.exe - type: splunk -id: 2b1800dd-92f9-47dd-a981-fdf13w1q5d55 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-02-03' -name: Suspicious MSHTA Activity narrative: 'One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution @@ -35,14 +18,14 @@ narrative: 'One common adversary tactic is to bypass application white-listing s The searches in this story help you detect and investigate suspicious activity that may indicate that an attacker is leveraging mshta.exe to execute malicious code.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://redcanary.com/blog/windows-registry-attacks-threat-detection/ - - https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5 - - https://attack.mitre.org/wiki/Technique/T1170 -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.1' +- https://redcanary.com/blog/windows-registry-attacks-threat-detection/ +- https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5 +- https://attack.mitre.org/wiki/Technique/T1170 +tags: + analytics_story: Suspicious MSHTA Activity + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_okta_activities.yml b/stories/suspicious_okta_activities.yml deleted file mode 100644 index fd5ba53fad..0000000000 --- a/stories/suspicious_okta_activities.yml +++ /dev/null @@ -1,55 +0,0 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2020-04-02' -description: Monitor your Okta environment for suspicious activities. Due to the Covid - outbreak, many users are migrating over to leverage cloud services more and more. - Okta is a popular tool to manage multiple users and the web-based applications they need - to stay productive. The searches in this story will help monitor your Okta environment for - suspicious activities and associated user behaviors. -detections: - - detection_id: 371a6545-2618-4032-ad84-93386b8698c5 - name: Okta Failed SSO Attempts - type: splunk - - detection_id: 19cba45f-cad3-4032-8911-0c09e0444552 - name: Multiple Okta Users With Invalid Credentails From The Same IP - type: splunk - - detection_id: 62b70968-a0a5-4724-8ac4-67871e6f544d - name: Okta Account Lockout Events - type: splunk - - detection_id: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8 - name: Okta User Logins From Multiple Cities - type: splunk -id: 9cbd34af-8f39-4476-a423-bacd126c750b -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2020-04-02' -name: Suspicious Okta Activity -narrative: Okta is the leading single sign on (SSO) provider, allowing users to authenticate once - to Okta, and from there access a variety of web-based applications. These applications are - assigned to users and allow administrators to centrally manage which users are allowed to - access which applications. It also provides centralized logging to help understand how the - applications are used and by whom. \ - - While SSO is a major convenience for users, it also provides attackers with an opportunity. - If the attacker can gain access to Okta, they can access a variety of applications. - As such monitoring the environment is important. \ - - With people moving quickly to adopt web-based applications and ways to manage them, many - are still struggling to understand how best to monitor these environments. This analytic - story provides searches to help monitor this environment, and identify events and activity - that warrant further investigation such as credential stuffing or password spraying attacks, - and users logging in from multiple locations when travel is disallowed. -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -references: - - https://attack.mitre.org/wiki/Technique/T1078 - - https://owasp.org/www-community/attacks/Credential_stuffing - - https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work -spec_version: 2 -usecase: Security Monitoring -version: '1.0' diff --git a/stories/suspicious_okta_activity.yml b/stories/suspicious_okta_activity.yml new file mode 100644 index 0000000000..2d8b9d820c --- /dev/null +++ b/stories/suspicious_okta_activity.yml @@ -0,0 +1,36 @@ +name: Suspicious Okta Activity +id: 9cbd34af-8f39-4476-a423-bacd126c750b +version: 1 +date: '2020-04-02' +description: Monitor your Okta environment for suspicious activities. Due to the Covid + outbreak, many users are migrating over to leverage cloud services more and more. + Okta is a popular tool to manage multiple users and the web-based applications they + need to stay productive. The searches in this story will help monitor your Okta + environment for suspicious activities and associated user behaviors. +narrative: 'Okta is the leading single sign on (SSO) provider, allowing users to authenticate + once to Okta, and from there access a variety of web-based applications. These applications + are assigned to users and allow administrators to centrally manage which users are + allowed to access which applications. It also provides centralized logging to help + understand how the applications are used and by whom. \ + + While SSO is a major convenience for users, it also provides attackers with an opportunity. + If the attacker can gain access to Okta, they can access a variety of applications. + As such monitoring the environment is important. \ + + With people moving quickly to adopt web-based applications and ways to manage them, + many are still struggling to understand how best to monitor these environments. + This analytic story provides searches to help monitor this environment, and identify + events and activity that warrant further investigation such as credential stuffing + or password spraying attacks, and users logging in from multiple locations when + travel is disallowed.' +author: Rico Valdez, Splunk +type: ESCU +references: +- https://attack.mitre.org/wiki/Technique/T1078 +- https://owasp.org/www-community/attacks/Credential_stuffing +- https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work +tags: + analytics_story: Suspicious Okta Activity + usecase: Security Monitoring + category: + - Adversary Tactics diff --git a/stories/suspicious_windows_registry_activities.yml b/stories/suspicious_windows_registry_activities.yml index b1d42e9710..f079fd1f25 100644 --- a/stories/suspicious_windows_registry_activities.yml +++ b/stories/suspicious_windows_registry_activities.yml @@ -1,41 +1,9 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2018-05-31' +name: Suspicious Windows Registry Activities +id: 2b1800dd-92f9-47dd-a981-fdf1351e5d55 +version: 1 +date: '2018-05-31' description: Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. -detections: - - detection_id: c9f4b923-f8af-4155-b697-1354f5dcbc5e - name: Remote Registry Key modifications - type: splunk - - detection_id: 1b989a0e-0129-4446-a695-f193a5b746fc - name: Suspicious Changes to File Associations - type: splunk - - detection_id: bbc644bc-37df-4e1a-9c88-ec9a53e2038c - name: Disabling Remote User Account Control - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb - name: Registry Keys for Creating SHIM Databases - type: splunk - - detection_id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc - name: Monitor Registry Keys for Print Monitors - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6b5264x9f - name: Reg.exe used to hide files/directories via registry keys - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: c9f4b923-f8af-4155-b697-1354f5bcbc5e - name: Registry Keys Used For Privilege Escalation - type: splunk -id: 2b1800dd-92f9-47dd-a981-fdf1351e5d55 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-31' -name: Suspicious Windows Registry Activities narrative: "Attackers are developing increasingly sophisticated techniques for hijacking\ \ target servers, while evading detection. One such technique that has become progressively\ \ more common is registry modification.\\\n The registry is a key component of the\ @@ -45,13 +13,13 @@ narrative: "Attackers are developing increasingly sophisticated techniques for h \ administrator-level privileges, maintain persistence, and move laterally within\ \ the environment.\\\n The searches in this story are designed to help you detect\ \ behaviors associated with manipulation of the Windows registry." -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://redcanary.com/blog/windows-registry-attacks-threat-detection/ - - https://attack.mitre.org/wiki/Technique/T1112 -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://redcanary.com/blog/windows-registry-attacks-threat-detection/ +- https://attack.mitre.org/wiki/Technique/T1112 +tags: + analytics_story: Suspicious Windows Registry Activities + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/suspicious_wmi.yml b/stories/suspicious_wmi_use.yml similarity index 50% rename from stories/suspicious_wmi.yml rename to stories/suspicious_wmi_use.yml index 74e4da46da..b46d55757e 100644 --- a/stories/suspicious_wmi.yml +++ b/stories/suspicious_wmi_use.yml @@ -1,41 +1,12 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-01-13' +name: Suspicious WMI Use +id: c8ddc5be-69bc-4202-b3ab-4010b27d7ad5 +version: 2 +date: '2018-10-23' description: Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. -detections: - - detection_id: 272df6de-61f1-4784-877c-1fbc3e2d0838 - name: Remote WMI Command Attempt - type: splunk - - detection_id: d25d2c3d-d9d8-40ec-8fdf-e86fe155a3da - name: Remote Process Instantiation via WMI - type: splunk - - detection_id: 71bfdb13-f200-4c6c-b2c9-a2e07adf437d - name: WMI Permanent Event Subscription - type: splunk - - detection_id: ad05aae6-3b2a-4f73-af97-57bd26cee3b9 - name: WMI Permanent Event Subscription - Sysmon - type: splunk - - detection_id: 38cbd42c-1098-41bb-99cf-9d6d2b296d83 - name: WMI Temporary Event Subscription - type: splunk - - detection_id: 24869767-8579-485d-9a4f-d9ddfd8f0cac - name: Process Execution via WMI - type: splunk - - detection_id: aa73f80d-d728-4077-b226-81ea0c8be589 - name: Script Execution via WMI - type: splunk -id: c8ddc5be-69bc-4202-b3ab-4010b27d7ad5 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2018-10-23' -name: Suspicious WMI Use narrative: 'WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning @@ -50,13 +21,13 @@ narrative: 'WMI is a Microsoft infrastructure for management data and operations In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end.' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf - - https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' +- https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf +- https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html +tags: + analytics_story: Suspicious WMI Use + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/aws_ec2_modifications.yml b/stories/unusual_aws_ec2_modifications.yml similarity index 67% rename from stories/aws_ec2_modifications.yml rename to stories/unusual_aws_ec2_modifications.yml index 36f409a652..03f4c1edac 100644 --- a/stories/aws_ec2_modifications.yml +++ b/stories/unusual_aws_ec2_modifications.yml @@ -1,21 +1,10 @@ -category: - - Cloud Security -channel: ESCU -creation_date: '2018-04-09' +name: Unusual AWS EC2 Modifications +id: 73de57ef-0dfc-411f-b1e7-fa24428aeae0 +version: 1 +date: '2018-04-09' description: Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. -detections: - - detection_id: 56f91724-cf3f-4666-84e1-e3712fb41e76 - name: EC2 Instance Modified With Previously Unseen User - type: splunk -id: 73de57ef-0dfc-411f-b1e7-fa24428aeae0 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2018-04-09' -name: Unusual AWS EC2 Modifications narrative: "A common attack technique is to infiltrate a cloud instance and make modifications.\ \ The adversary can then secure access to your infrastructure or hide their activities.\ \ So it's important to stay alert to changes that may indicate that your environment\ @@ -25,12 +14,12 @@ narrative: "A common attack technique is to infiltrate a cloud instance and make \ or by known users who modify or create instances in a way that have not been done\ \ before. This story also provides investigative searches that help you go deeper\ \ once you detect suspicious behavior." -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf +tags: + analytics_story: Unusual AWS EC2 Modifications + usecase: Security Monitoring + category: + - Cloud Security diff --git a/stories/unusual_processes.yml b/stories/unusual_processes.yml index e845c8d852..173d644caa 100644 --- a/stories/unusual_processes.yml +++ b/stories/unusual_processes.yml @@ -1,40 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2016-08-09' +name: Unusual Processes +id: f4368e3f-d59f-4192-84f6-748ac5a3ddb6 +version: 2 +date: '2020-02-04' description: Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. -detections: - - detection_id: 29ccce64-a10c-4389-a45f-337cb29ba1f7 - name: Uncommon Processes On Endpoint - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6a4264e7f - name: Unusually Long Command Line - type: splunk - - detection_id: 57edaefa-a73b-45e5-bbae-f39c1473f941 - name: Unusually Long Command Line - MLTK - type: splunk - - detection_id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac - name: Detect Rare Executables - type: splunk - - detection_id: a34aae96-ccf8-4aef-952c-3ea21444444d - name: System Processes Run From Unexpected Locations - type: splunk - - detection_id: 6c135f8d-5e60-454e-80b7-c56eed739833 - name: RunDLL Loading DLL By Ordinal - type: splunk - - detection_id: a51bfe1a-94f0-48cc-b1e4-16ae10145893 - name: Detect processes used for System Network Configuration Discovery - type: splunk -id: f4368e3f-d59f-4192-84f6-748ac5a3ddb6 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2020-02-04' -name: Unusual Processes narrative: 'Being able to profile a host''s processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ @@ -48,14 +19,14 @@ narrative: 'Being able to profile a host''s processes within your environment ca how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity.' -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html - - https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf - - https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.1' +- https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html +- https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf +- https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262 +tags: + analytics_story: Unusual Processes + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/non-secure_communications.yml b/stories/use_of_cleartext_protocols.yml similarity index 60% rename from stories/non-secure_communications.yml rename to stories/use_of_cleartext_protocols.yml index 5e35634829..279d6984c9 100644 --- a/stories/non-secure_communications.yml +++ b/stories/use_of_cleartext_protocols.yml @@ -1,20 +1,9 @@ -category: - - Best Practices -channel: ESCU -creation_date: '2016-09-13' +name: Use of Cleartext Protocols +id: 826e6431-aeef-41b4-9fc0-6d0985d65a21 +version: 1 +date: '2017-09-15' description: Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. -detections: - - detection_id: 6923cd64-17a0-453c-b945-81ac2d8c6db9 - name: Protocols passing authentication in cleartext - type: splunk -id: 826e6431-aeef-41b4-9fc0-6d0985d65a21 -maintainers: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2017-09-15' -name: Use of Cleartext Protocols narrative: Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information @@ -22,12 +11,12 @@ narrative: Various legacy protocols operate by default in the clear, without the protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. -original_authors: - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel +author: Bhavin Patel, Splunk +type: ESCU references: - - https://www.monkey.org/~dugsong/dsniff/ -spec_version: 2 -usecase: Security Monitoring -version: '1.0' +- https://www.monkey.org/~dugsong/dsniff/ +tags: + analytics_story: Use of Cleartext Protocols + usecase: Security Monitoring + category: + - Best Practices diff --git a/stories/web_fraud.yml b/stories/web_fraud_detection.yml similarity index 74% rename from stories/web_fraud.yml rename to stories/web_fraud_detection.yml index 46e6421a2c..d02f1f1ff6 100644 --- a/stories/web_fraud.yml +++ b/stories/web_fraud_detection.yml @@ -1,26 +1,9 @@ -category: - - Abuse -channel: ESCU -creation_date: '2018-07-12' +name: Web Fraud Detection +id: 31337aaa-bc22-4752-b599-ef112dq1dq7a +version: 1 +date: '2018-10-08' description: Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. -detections: - - detection_id: 31337aaa-941d-4ada-81ac-q2a17be5bf0d - name: Web Fraud - Account Harvesting - type: splunk - - detection_id: 31337bbb-bc22-4752-b599-ef192df2dc7a - name: Web Fraud - Anomalous User Clickspeed - type: splunk - - detection_id: 31337a1a-53b9-4e05-96e9-55c934cb71d3 - name: Web Fraud - Password Sharing Across Accounts - type: splunk -id: 31337aaa-bc22-4752-b599-ef112dq1dq7a -maintainers: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger -modification_date: '2018-10-08' -name: Web Fraud Detection narrative: 'The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes @@ -54,13 +37,13 @@ narrative: 'The Federal Bureau of Investigations (FBI) defines Internet fraud as Another search detects incidents wherein a single password is used across multiple accounts, which may indicate that a fraudster has infiltrated your environment and embedded a common password within a script.' -original_authors: - - company: Splunk - email: Mayhem@splunk.com - name: Jim Apger +author: Jim Apger, Splunk +type: ESCU references: - - https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud - - https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 -spec_version: 2 -usecase: Fraud Detection -version: '1.0' +- https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud +- https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718 +tags: + analytics_story: Web Fraud Detection + usecase: Fraud Detection + category: + - Abuse diff --git a/stories/windows_defense_evasion_tactics.yml b/stories/windows_defense_evasion_tactics.yml new file mode 100644 index 0000000000..2958e60dc1 --- /dev/null +++ b/stories/windows_defense_evasion_tactics.yml @@ -0,0 +1,21 @@ +name: Windows Defense Evasion Tactics +id: 56e24a28-5003-4047-b2db-e8f3c4618064 +version: 1 +date: '2018-05-31' +description: 'Detect tactics used by malware to evade defenses on Windows endpoints. + A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` + and disabling user-account control, among many others ' +narrative: Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that + adversaries employ in a variety of ways to bypass or defeat defensive security measures. + There are many techniques enumerated by the MITRE ATT&CK framework that are applicable + in this context. This Analytic Story includes searches designed to identify the + use of such techniques on Windows platforms. +author: David Dorsey, Splunk +type: ESCU +references: +- https://attack.mitre.org/wiki/Defense_Evasion +tags: + analytics_story: Windows Defense Evasion Tactics + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/file_extension_abuse.yml b/stories/windows_file_extension_and_association_abuse.yml similarity index 75% rename from stories/file_extension_abuse.yml rename to stories/windows_file_extension_and_association_abuse.yml index 256163145f..84c7e88da1 100644 --- a/stories/file_extension_abuse.yml +++ b/stories/windows_file_extension_and_association_abuse.yml @@ -1,28 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2018-01-26' +name: Windows File Extension and Association Abuse +id: 30552a76-ac78-48e4-b3c0-de4e34e9563d +version: 1 +date: '2018-01-26' description: Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. -detections: - - detection_id: b06a555e-dce0-417d-a2eb-28a5d8d66ef7 - name: Execution of File with Multiple Extensions - type: splunk - - detection_id: ab0353e6-a956-420b-b724-a8b4846d5d5a - name: Execution of File With Spaces Before Extension - type: splunk - - detection_id: 1b989a0e-0129-4446-a695-f193a5b746fc - name: Suspicious Changes to File Associations - type: splunk -id: 30552a76-ac78-48e4-b3c0-de4e34e9563d -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2018-01-26' -name: Windows File Extension and Association Abuse narrative: "Attackers use a variety of techniques to entice users to run malicious\ \ code or to persist on an endpoint. One way to accomplish these goals is to leverage\ \ file extensions and the mechanism Windows uses to associate files with specific\ @@ -48,13 +31,13 @@ narrative: "Attackers use a variety of techniques to entice users to run malicio \ the fact that something suspicious has occurred.\\\nRun the searches in this story\ \ to detect and investigate suspicious behavior that may indicate abuse or manipulation\ \ of Windows file extensions and/or associations." -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ - - https://attack.mitre.org/wiki/Technique/T1042 -spec_version: 2 -usecase: Advanced Threat Detection -version: '1.0' +- https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/ +- https://attack.mitre.org/wiki/Technique/T1042 +tags: + analytics_story: Windows File Extension and Association Abuse + usecase: Advanced Threat Detection + category: + - Malware diff --git a/stories/suspicious_event_log_activity.yml b/stories/windows_log_manipulation.yml similarity index 58% rename from stories/suspicious_event_log_activity.yml rename to stories/windows_log_manipulation.yml index b2d9f85f43..18fdf81a66 100644 --- a/stories/suspicious_event_log_activity.yml +++ b/stories/windows_log_manipulation.yml @@ -1,30 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-02-17' +name: Windows Log Manipulation +id: b6db2c60-a281-48b4-95f1-2cd99ed56835 +version: 2 +date: '2017-09-12' description: Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. -detections: - - detection_id: b89919ed-ee5f-492c-b139-95dbb162039e - name: Deleting Shadow Copies - type: splunk - - detection_id: ad517544-aff9-4c96-bd99-d6eb43bfbb6a - name: Windows Event Log Cleared - type: splunk - - detection_id: 2827c0fd-e1be-4868-ae25-59d28e0f9d4f - name: Suspicious wevtutil Usage - type: splunk - - detection_id: b6e0ff70-b122-4227-9368-4cf322ab43c3 - name: USN Journal Deletion - type: splunk -id: b6db2c60-a281-48b4-95f1-2cd99ed56835 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-09-12' -name: Windows Log Manipulation narrative: 'Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still @@ -39,14 +19,14 @@ narrative: 'Because attackers often modify system logs to cover their tracks and Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified).' -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ - - https://zeltser.com/security-incident-log-review-checklist/ - - http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html -spec_version: 2 -usecase: Security Monitoring -version: '2.0' +- https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/ +- https://zeltser.com/security-incident-log-review-checklist/ +- http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html +tags: + analytics_story: Windows Log Manipulation + usecase: Security Monitoring + category: + - Adversary Tactics diff --git a/stories/windows_persistence.yml b/stories/windows_persistence.yml deleted file mode 100644 index 6d679379f3..0000000000 --- a/stories/windows_persistence.yml +++ /dev/null @@ -1,74 +0,0 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-04-19' -description: Monitor for activities and techniques associated with maintaining persistence - on a Windows system--a sign that an adversary may have compromised your environment. -detections: - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01bbb - name: Registry Keys for Creating SHIM Databases - type: splunk - - detection_id: 404620de-46d8-48b6-90cc-8a8d7b0876a3 - name: Shim Database Installation With Suspicious Parameters - type: splunk - - detection_id: 6e4c4588-ba2f-42fa-97e6-9f6f548eaa33 - name: Shim Database File Creation - type: splunk - - detection_id: f5f6af30-7aa7-4295-bfe9-07fe87c01a4b - name: Registry Keys Used For Persistence - type: splunk - - detection_id: 1297fb80-f42a-4b4a-9c8a-88c066437cf6 - name: Schtasks used for forcing a reboot - type: splunk - - detection_id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d - name: Sc.exe Manipulating Windows Services - type: splunk - - detection_id: 8470d755-0c13-45b3-bd63-387a373c10cf - name: Reg.exe Manipulating Windows Services Registry Keys - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6b5264g9f - name: Hiding Files And Directories With Attrib.exe - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6b5264x9f - name: Reg.exe used to hide files/directories via registry keys - type: splunk - - detection_id: c77162d3-f93c-45cc-80c8-22f6v5264g9f - name: Detect Path Interception By Creation Of program.exe - type: splunk - - detection_id: f5f6af30-7ba7-4295-bfe9-07de87c01bbc - name: Monitor Registry Keys for Print Monitors - type: splunk - - detection_id: c9f4b923-f8af-4155-b697-1354f5dcbc5e - name: Remote Registry Key modifications - type: splunk -id: 30874d4f-20a1-488f-85ec-5d52ef74e3f9 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -modification_date: '2018-05-31' -name: Windows Persistence Techniques -narrative: Maintaining persistence is one of the first steps taken by attackers after - the initial compromise. Attackers leverage various custom and built-in tools to - ensure survivability and persistent access within a compromised enterprise. This - Analytic Story provides searches to help you identify various behaviors used by - attackers to maintain persistent access to a Windows environment. -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey - - company: Splunk - email: bpatel@splunk.com - name: Bhavin Patel -references: - - http://www.fuzzysecurity.com/tutorials/19.html - - https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html - - http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/ - - https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html - - https://www.youtube.com/watch?v=dq2Hv7J9fvk -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' diff --git a/stories/windows_persistence_techniques.yml b/stories/windows_persistence_techniques.yml new file mode 100644 index 0000000000..c05d4d7b4a --- /dev/null +++ b/stories/windows_persistence_techniques.yml @@ -0,0 +1,24 @@ +name: Windows Persistence Techniques +id: 30874d4f-20a1-488f-85ec-5d52ef74e3f9 +version: 2 +date: '2018-05-31' +description: Monitor for activities and techniques associated with maintaining persistence + on a Windows system--a sign that an adversary may have compromised your environment. +narrative: Maintaining persistence is one of the first steps taken by attackers after + the initial compromise. Attackers leverage various custom and built-in tools to + ensure survivability and persistent access within a compromised enterprise. This + Analytic Story provides searches to help you identify various behaviors used by + attackers to maintain persistent access to a Windows environment. +author: Bhavin Patel, Splunk +type: ESCU +references: +- http://www.fuzzysecurity.com/tutorials/19.html +- https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html +- http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/ +- https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html +- https://www.youtube.com/watch?v=dq2Hv7J9fvk +tags: + analytics_story: Windows Persistence Techniques + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/windows_privilege_escalation.yml b/stories/windows_privilege_escalation.yml index 57ba3c1d01..470196052b 100644 --- a/stories/windows_privilege_escalation.yml +++ b/stories/windows_privilege_escalation.yml @@ -1,30 +1,10 @@ -category: - - Adversary Tactics -channel: ESCU -creation_date: '2017-12-07' +name: Windows Privilege Escalation +id: 644e22d3-598a-429c-a007-16fdb802cae5 +version: 2 +date: '2020-02-04' description: Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. -detections: - - detection_id: 13c2f6c3-10c5-4deb-9ba1-7c4460ebe4ae - name: Overwriting Accessibility Binaries - type: splunk - - detection_id: c9f4b923-f8af-4155-b697-1354f5bcbc5e - name: Registry Keys Used For Privilege Escalation - type: splunk - - detection_id: 29ccce64-a10c-4389-a45f-337cb29ba1f7 - name: Uncommon Processes On Endpoint - type: splunk - - detection_id: aa0c4aeb-5b18-41c4-8c07-f1442d7599df - name: Child Processes of Spoolsv.exe - type: splunk -id: 644e22d3-598a-429c-a007-16fdb802cae5 -maintainers: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey -modification_date: '2020-02-04' -name: Windows Privilege Escalation narrative: 'Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such @@ -33,12 +13,12 @@ narrative: 'Privilege escalation is a "land-and-expand" technique, wherein an ad control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment.' -original_authors: - - company: Splunk - email: davidd@splunk.com - name: David Dorsey +author: David Dorsey, Splunk +type: ESCU references: - - https://attack.mitre.org/tactics/TA0004/ -spec_version: 2 -usecase: Advanced Threat Detection -version: '2.0' +- https://attack.mitre.org/tactics/TA0004/ +tags: + analytics_story: Windows Privilege Escalation + usecase: Advanced Threat Detection + category: + - Adversary Tactics diff --git a/stories/service_abuse.yml b/stories/windows_service_abuse.yml similarity index 57% rename from stories/service_abuse.yml rename to stories/windows_service_abuse.yml index c3cdb338fa..f30090c6b2 100644 --- a/stories/service_abuse.yml +++ b/stories/windows_service_abuse.yml @@ -1,28 +1,11 @@ -category: - - Malware -channel: ESCU -creation_date: '2017-11-02' +name: Windows Service Abuse +id: 6dbd810e-f66d-414b-8dfc-e46de55cbfe2 +version: 3 +date: '2017-11-02' description: Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. -detections: - - detection_id: f0c693d8-2a89-4ce7-80b4-98fea4c3ea6d - name: Sc.exe Manipulating Windows Services - type: splunk - - detection_id: 8470d755-0c13-45b3-bd63-387a373c10cf - name: Reg.exe Manipulating Windows Services Registry Keys - type: splunk - - detection_id: 823136f2-d755-4b6d-ae04-372b486a5808 - name: First Time Seen Running Windows Service - type: splunk -id: 6dbd810e-f66d-414b-8dfc-e46de55cbfe2 -maintainers: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez -modification_date: '2017-11-02' -name: Windows Service Abuse narrative: The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to @@ -32,13 +15,13 @@ narrative: The Windows operating system uses a services architecture to allow fo opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. -original_authors: - - company: Splunk - email: rvaldez@splunk.com - name: Rico Valdez +author: Rico Valdez, Splunk +type: ESCU references: - - https://attack.mitre.org/wiki/Technique/T1050 - - https://attack.mitre.org/wiki/Technique/T1031 -spec_version: 2 -usecase: Advanced Threat Detection -version: '3.0' +- https://attack.mitre.org/wiki/Technique/T1050 +- https://attack.mitre.org/wiki/Technique/T1031 +tags: + analytics_story: Windows Service Abuse + usecase: Advanced Threat Detection + category: + - Malware